Role of Cavitation during Bulk Ultrasound Ablation: Ex vivo and In Vivo Studies
A dissertation submitted to
Graduate School at the University of Cincinnati
in the partial fulfillment of the requirements for the degree of
Doctor of Philosophy
in Biomedical Engineering at the University of Cincinnati
by
Chandrapriya Karunakaran
July 2012
Dissertation Advisor T. Douglas Mast, PhD
Abstract
Bubble activity can complicate ultrasound treatment by shielding ultrasound energy from the focus or by increasing local ultrasound absorption, rendering the treatment unpredictable. In this thesis, the role of bubble activity was evaluated for ex vivo and in vivo bulk ultrasound ablation experiments.
Overpressure was used to suppress cavitation and tissue vaporization in the ex vivo studies.
Ex vivo bovine liver was ablated with unfocused ultrasound (3.1 MHz or 4.8 MHz) at 31 W/cm2 for 10 or 20 minutes. A passive cavitation detector (PCD) was used to record acoustic emissions throughout the treatment. Subharmonic, broadband and low-frequency emissions were quantified by processing signals recorded by the PCD. The treated liver was sliced and stained with 2% triphenyl tetrazolium chloride (TTC) to evaluate lesion geometry. Multivariate multiple regression models were computed to predict lesion areas and depths based on the three acoustic emission levels. Results confirm that the three acoustic emissions were significant in predicting lesion dimensions. It was concluded that subharmonic activity may increase lesion area by redistributing ultrasound energy locally, while tissue vaporization may decrease lesion area and depth by shielding energy from the treatment zone.
TTC uptake of ultrasound treated tissue was compared to TTC uptake of thermally heated tissue, with comparable thermal doses, to evaluate the contribution of nonthermal bioeffects in bulk ultrasound ablation. Logistic regression modeling was performed to estimate and compare the probability of tissue coagulation, for range of thermal doses, among the two experimental groups (ultrasound and thermally heated). Results suggested no significant differences between
TTC uptake levels of thermally heated and ultrasound treated tissue, suggesting that cavitation during bulk ultrasound ablation may translate to thermal bioeffects evident in the tissue.
2 To validate TTC staining as a method to evaluate ultrasound treatment success, tissue from multiple TTC uptake regions of ultrasound ablated liver and VX2 tumor were stained with
4', 6-diamidino-2-phenylindole (DAPI). Nuclear size distributions among the three TTC uptake regions were quantified and compared to evaluate changes in histology among the three regions.
Results suggest that TTC uptake levels correspond to distinguishable differences in nuclear size distribution. Cells in the region of partial TTC uptake may undergo apoptosis.
The role of cavitation in ultrasound ablation in vivo was investigated by employing passive cavitation imaging to record and monitor cavitation during treatment of swine liver with ultrasound (2700-6000 W/cm2, 20 sec-2 minutes). Passive cavitation imaging can spatially and temporally resolve cavitation signals including subharmonic, broadband and harmonic emissions.
Spatial localization of subharmonic, broadband, low-frequency and harmonic activity was compared to local tissue ablation through receiver operating characteristic (ROC) curve analysis.
The results suggest that for these in vivo experiments, with no significant subharmonic or broadband emissions, harmonic emissions were the most significant in predicting tissue ablation.
Results from this thesis suggest that cavitation can play opposing roles in bulk ultrasound ablation. Cavitation detection and imaging can be used to predict thermal lesion formation. TTC uptake levels correlate to thermal dose and thermal bioeffects in ultrasound ablation and can be used to evaluate treatment success. The results from this thesis can be extended to aid in planning and monitoring of ultrasound treatments and increase the efficiency of ultrasound ablation.
3
Acknowledgements
This work was supported by NIH grants R43 CA124283, R21 EB008483, University of
Cincinnati College of Medicine Dean’s Bridge Fund and University of Cincinnati Summer
Graduate Research Fellowship.
First and foremost I would like to thank my advisor Dr. T. Douglas Mast who has always encouraged and supported me in pursuing new ideas. He has taught me to be an independent researcher starting from forming an idea, to planning experiments and problem solving. I have learnt a lot about medical ultrasound and signal processing from him. I appreciate his time, effort and financial support through out my doctoral research experience. I would also like to thank Dr.
Christy K. Holland who was always been available to provide her insightful thoughts and suggestions to improve the project. I would like to thank Dr. Daria Narmoneva for help and guidance with histology techniques and processing. I appreciate and thank Dr. Marepalli B. Rao who was always available to provide support with statistical methods and processing relevant to the project in spite of his busy schedule. The scientifically critical comments and suggestions provide by all the committee members have been instrumental in improving the project and streamlining the outcome of the research.
I am grateful to all my colleagues and friends in biomedical engineering. I appreciate the assistance of my lab mates Dr. Vasant Salgaonkar, Swetha Subramanian, Anna Nagle, Kyle Rich for all the discussions and suggestions. I would like to appreciate Mark Burgess, Amel Alqaddah and Molly Perdix for their help with the experiments. A special thanks to Mr. Ronald Burrage for help in construction of overpressure chamber. A special thanks to all my friends in Cincinnati who have made this experience memorable by offering support and affection even without me asking for it.
4 Finally I would like to dedicate this thesis to my family including my parents Mr. & Mrs.
Karunakaran, my brother and my sister for their sacrifice, love and encouragement.
5 CONTENTS
1. Introduction
1.1 Background…………………………………………………………………………………..19 1.1.1 Liver cancer incidence…………………………………………………………...... 19 1.1.2 Liver cancer treatment options…………………………………………………………...20 1.1.2.1 Surgical resection ……………………………………………………...... 20 1.1.2.2 Percutaneous ethanol injection (PEI) ………………………………………………..20 1.1.2.3 Electrolytic ablation………………………………………………………………….20 1.1.2.4 Cryosurgery …………………………………………………………..……………...21 1.1.2.5 Liver transplantation ………………………………………………………………...21 1.1.2.6 Thermal ablation ……………………………………………………………………21 1.1.3 Ultrasound ablation ……………………………………………………………………..23 1.1.3.1 Medical application of ultrasound ……………………………………………..……23 1.1.3.2 Bubble activity (cavitation and boiling) in ultrasound ablation …….…………….…24 1.1.3.3 Bioeffects of ultrasound ……………………………………………………………25
Thermal bioeffects
Cavitation bioeffects 1.1.3.4 Role of cavitation in bulk ultrasound ablation ………………………………………27 1.1.3.5 Histology of ultrasound treated tissue ……………………………………………….28 1.2 Research objectives…………………………………………………………………………..28 1.3 Hypothesis and specific aims………………………………………………………………...29 1.4 Thesis organization…………………………………………………………………………..30
6 2. Effect of overpressure on acoustic emissions and lesion histology in bulk
ultrasound ablation: ex vivo
2.1 Objectives …………………………………………………………………………………...32 2.2 Materials and methods ………………………………………………………………...... 33 2.2.1 Design of overpressure chamber……………………………………………………..…..33
2.2.2 Acoustic properties of the chamber ……………………………………………………..34
2.2.3 Ultrasound image-treat probes…………………………………………………………...38
2.2.4 Tissue preparation and handling ………………………………………………………...40
2.2.5 Experimental setup……………………………………………………………………….40
2.2.6 Ultrasound ablation ……………………………………………………………………...41
2.2.7 Data acquisition………………………………………………………………………….42
2.2.8 Histology evaluation …………………………………………………………………….42
2.3 Data processing………………………………………………………………………………43
2.3.1 Spectral leakage processing ……………………………………………………………..44
2.4 Statistical analysis …………………………………………………………………………...47
2.5 Results ……………………………………………………………………………………….48
2.5.1 Experimental results at 3.1 MHz ………………………………………………………….48
2.5.2 Experimental results at 4.8 MHz ………………………………………………………….56
2.6 Discussion …………………………………………………………………………………...67
2.6.1 Experiments at 3.1 MHz………. ………………………………………………………….67
2.6.2 Experiments at 4.8 MHz………..………………………………………………………….70
2.6.3 Comparing 3.1 MHz vs. 4.8 MHz experiments …………………………………………...72
2.7 Conclusion …………………………………………………………………………………..73
7 3. Role of thermal and non-thermal mechanisms in tissue bioeffects for bulk
ultrasound ablation
3.1 Objective …………………………………………………………………………………….75
3.2 Materials and methods ………………………………………………………………………77
3.2.1 TTC Vs. thermal dose …………………………………………………………………...77
3.2.2 Calibration of analog water heater ………………………………………………………79
3.2.3 Replicating ultrasound thermal dose……………………………………………………..82
3.2.4 TTC uptake scores ………………………………………………………………………84
3.2.5 Proportional odds model ………………………………………………………………...85
3.3 Results………………………………………………………………………..………………87
3.3.1 TTC uptake vs. cumulative thermal dose ……………………………………………….87
3.3.2 Matching ultrasound thermal dose……………………………………………………….89
3.3.3 Proportional odds model ………………………………………………………………...94
3.4 Discussion …………………………………………………………………………………...98
3.4.1 TTC as an indicator of thermal dose …………………………………………………….98
3.4.2 Role of cavitation in altering tissue bioeffects …………………………………………..99
3.5 Conclusion …………………………………………………………………………………100
4. Relations between nuclear histology and TTC uptake for liver and tumor
tissue treated by bulk ultrasound ablation in vivo
4.1 Objective …………………………………………………………………………………...101
4.2 Materials and methods……………………………………………………………………...102
4.2.1 Tumor implantation ……………………………………………………………………103
4.2.2 Ultrasound ablation……………………………………………………………………..103
8 4.2.3 Histology staining ……………………………………………………………………..104
4.2.4 Image processing ………………………………………………………………………114
4.3 Results……………………………………………………………………………………...117
4.4 Discussion………………………………………………………………………………….130
4.5 Conclusion………………………………………………………………………………….131
5. Correlation of passive cavitation images and lesion histology for in vivo
bulk ultrasound ablation
5.1 Objective …………………………………………………………………………………...132
5.2 Materials and methods ……………………………………………………………………..133
5.2.1 Transducer ……………………………………………………………………………...133
5.2.2 Aligning therapy and imaging transducers ………………………………………….....135
5.2.3 Experimental methods …………………………………………………………………135
5.2.3.1 Passive cavitation imaging ……………………………………………………………..136
5.2.4 Data processing ………………………………………………………………………...138
5.2.5 TTC staining ……………………………………………………………………………..141
5.2.6 Statistical analysis ………………………………………………………………………..141
5.2.6.1 Receiver operating characteristics analysis ……………………………………….…...142
5.3 Results ……………………………………………………………………………………...145
5.4 Discussion ………………………………………………………………………………….159
5.5 Conclusion …………………………………………………………………………………161
6. Conclusions and future work
6.1 Conclusions…………………………………………………………………………………163
6.2 Future work………………………………………………………………………………....166
9 Figure Captions
2.1 Power transmission coefficient (equation 2.2) of the PET bottle for a range of attenuation
coefficients at 3.5 MHz (a) and 4.8 MHz (b). The cursors indicate the attenuation
coefficients for which theoretical transmission coefficients matched the measured
values………………………………………………………………………………………36
2.2 Reflection and transmission coefficients for the PET bottle at 3.1 MHz (a) and 4.8 MHz (b)
for range of wall thickness values………………………………………………………….37
2.3 Overpressure experimental setup. (a) Overpressure chamber assembly showing the
ultrasound array, the passive cavitation detector and the pressure chamber. (b) Iris system
with array imaging and therapy module and the host computer. (c) Block diagram of
driving electronics for the Iris system……...... 39
2.4 Image of ultrasound treated tissue showing thermocouple positions (white markers) and
associated needle tracks……………………………………………………………………42
2.5 TTC stained section of ultrasound treated tissue showing ablated (AA) and treated area
(TA), ablated (AD) and treated depth (TD) and thermocouple location…………………..43
2.6 Representative original and post processed low-frequency signals from a 4.8 MHz
experiment showing trace of low-frequency and broadband signal with spectral leakage (a)
and low-frequency after processing to remove spectral leakage (b)……………………….46
2.7 Representative spectra showing subharmonic, broadband and post-processed low-
frequency emission levels (dB) for control (a) and 1.2 MPa (b) experiments performed at
3.1 MHz. …………..………………………………………………………………………49
10 2.8 Representative acoustic emission traces showing subharmonic, broadband and post-
processed low-frequency emission levels (dB) for control (a) and 1.2 MPa (b) experiments
performed at 3.1 MHz……………………………………………………………………...50
2.9 Comparison of averaged subharmonic, broadband and low-frequency emission levels
among control and overpressure (1.2 MPa) runs for the 3.1 MHz experiments…………...51
2.10 Representative TTC stained sections of tissue treated under control (left) and 1.2 MPa
(right) conditions at 3.1 MHz………………………………………………………………52
2.11 Comparison of treatment areas (a) and depths (b) between control and overpressure (1.2
MPa) runs at 3.1 MHz……………………………………………………………………..53
2.12 Representative time spectrum plots for control (a), 0.52 MPa (b) and 1.2 MPa (c) trials at
4.8 MHz……………………………………………………………………………………58
2.13 Representative individual emission (subharmonic, broadband, post-processed low-
frequency) and temperature traces from control (a), 0.52 MPa (b) and 1.2 MPa (c) trials at
4.8 MHz……………………………………………………………………………………59
2.14 Comparison of averaged acoustic emission (subharmonic, broadband and low-frequency)
levels for control, 0.52 MPa and 1.2 MPa runs at 4.8 MHz……………………………….60
2.15 Maximum tissue temperatures attained in control, 0.52 MPa and 1.2 MPa condition for 4.8
MHz ablation experiments…………………………………………………………………61
2.16 Representative TTC stained sections from tissue ablated at 4.8 MHz under control, 0.52
MPa and 1.2 MPa conditions showing central ablated region in tan, ring of partially ablated
or partially viable tissue in pink and untreated viable tissue in red………………………..62
2.17 Comparison of treatment area (a) and treatment depth (b) among control, 0.52 MPa and 1.2
MPa conditions for the 4.8 MHz ablation experiments……………………………………63
11 2.18 Comparison of broadband emissions levels, when calculated as energy in the band 0.4-0.75
MHz, across control and 1.2 MPa experiments performed at 3.1 MHz…………………...68
3.1 Water bath setup showing tissue samples placed in the water bath and a PCD positioned to
record acoustic signals from the heated tissue…………………………………………….79
3.2 Experimental setup for water bath calibration showing the water-heater, water bath and the
steel lid…………………………………………………………………………………….80
3.3 Calibration curve for analog water bath heater at various dial settings…………………...81
3.4 Linear portion of calibration curves at various heater dial settings and linear regression line
showing the rate of water bath heating……………………………………………………82
3.5 Calibration curve for water bath calibration showing water and liver tissue temperatures
for each dial setting………………………………………………………………………..83
3.6 Tissue temperature and log10 cumulative thermal dose traces for four 4.8 MHz ultrasound
ablation trials………………………………………………………………………………86
3.7 Ultrasound treated tissue showing three TTC uptake regions scored from 0 to 3………...87
3.8 TTC stained liver tissue slices heated to various temperatures from 25 °C to 70 °C
showing slight decrease in TTC uptake after 43 °C and decrease in TTC uptake after
50 °C………………………………………………………………………………………88
3.9 Low-frequency (LF) and broadband (BB) emission levels from tissue heated in water bath
(C302) and control tissue maintained at room temperature (V302)………………………89
3.10 Tissue temperature, log10 cumulative thermal dose curve (a) and TTC stained tissue
sections for ultrasound trial 1 (red) and matched water bath experiments (green solid and
dashed)…………………………………………………………………………………….91
12 3.11 Tissue temperature, log10 cumulative thermal dose curve (a) and TTC stained tissue
section (b) for ultrasound trial 2 (red) and matched water bath experiments
(green)……………………………………………………………………………………..92
3.12 Tissue temperature, log10 cumulative thermal dose curves (a) and TTC stained tissue
section (b) for ultrasound trial 3 (red) and matched water bath experiments (green).Tissue
temperature, log10 cumulative thermal dose curves (a) and TTC stained tissue sections (b)
for ultrasound trial 4 (red) and matched water bath experiment (green)………………….93
3.13 Tissue temperature, log10 cumulative thermal dose curves (a) and TTC stained tissue
sections (b) for ultrasound trial 4 (red) and matched water bath experiment (green)……..94
3.14 Summary of TTC scores for range of log10 cumulative thermal doses for ultrasound and
water bath experiments. ………………………………………………………………...... 95
3.15 Probability of tissue coagulation (top) and tissue remaining uncoagulated (bottom) for
range of log10 cumulative thermal dose for ultrasound and water bath
experiments…………………………………………………………………………….….97
3.16 Probability of tissue coagulation for range of log10 cumulative thermal dose values for
ultrasound (top) and water bath (bottom) experiments……………………………………98
4.1 VX2 tumor after 11-14 days of implantation (a). Experimental setup showing ultrasound
probe placed on rabbit liver during treatment (b)………………………………………..105
4.2 Representative sections of nonviable liver parenchyma (R27 ML liver), viable VX2 tumor
(R109 RL tumor), and nonviable VX2 tumor (R1 LL tumor) within rabbit liver, each
stained with 2% TTC solution…………………………………………………………..106
4.3 TTC stained slice and corresponding H&E section of rabbit liver (R27 RL liver) treated at
40.5 W/cm2 for 1 minute. TTC stained slice shows clear demarcation between viable and
13 nonviable tissue (a). H&E section with different zones of ablation marked on it
(b)………………………………………………………………………………………...107
4.4 Zones 1 through 5 with increasing cell death and necrosis as seen on H&E section at 40X
magnification for sample R27 RL liver……………………………………...... 109
4.5 TTC and H&E stained slices of rabbit liver (R27 ML liver) treated at 40.5 W/cm2 for 2
min showing zones 1 through 5………………………………………………………….110
4.6 Zones 1 through 5 with increasing cell death and necrosis as seen on H&E section at 40X
magnification for sample R27 ML liver…………………………………………………111
4.7 DAPI images (20X) of three regions on an in-vivo ultrasound ablated rabbit liver (sample
R1 LL liver). (a) Viable liver with round healthy nuclei, (b) partially viable region with
slightly elongated dying nuclei and (c) nonviable region with dead, scattered
nuclei……………………………………………………………………………………..112
4.8 DAPI images (40X) of viable (a) and nonviable (b) VX2 tumor (sample R26 RL tumor)
showing less numerous and smaller nuclei in the nonviable region…………………….113
4.9 DAPI images from (a) viable, (b) partially viable, and (c) nonviable liver before (left) and
after (right) image processing for sample R1 LL liver. …………………………………116
4.10 DAPI images from (a) viable and (b) nonviable tumor before (left) and after (right) image
processing for sample R26 RL tumor. …………………………………………………..117
4.11 TTC stained sections for 6 tissue samples showing viable, partially viable and nonviable
liver………………………………………………………………………………………118
4.12 Size histograms for viable, partially viable and nonviable regions of liver from samples
R110 RL liver (a) and R27 RL liver (b)…………………………………………………120
14 4.13 Size histograms for viable, partially viable and nonviable regions of liver from samples R1
LL liver (a) and R1 RL liver (b)……………………………………………………...... 121
4.14 Size histograms for viable and nonviable regions of liver from samples R26 RL liver (a)
and R27 ML liver (b)…………………………………………………………………….122
4.15 TTC stained sections for ultrasound treated (left) and untreated (right) VX2 tumor
samples…………………………………………………………………………………...124
4.16 Size histograms for viable and nonviable regions of VX2 tumor from sample R26 RL
VX2………………………………………………………………………………………125
4.17 Size histograms for the three viable (a) and nonviable (b) tumor samples. ……………..126
4.18 Comparison of number of nuclei in three size ranges for viable, partially viable, and
nonviable liver groups……………………………………………………………………128
4.19 Comparison of number of nuclei in three size ranges for viable and nonviable tumor
groups…………………………………………………………………………………….128
5.1 Experimental block diagram showing therapy and imaging transducer aligned with the
cone tip (a) and the setup for in vivo ablation (b)………………………………………..137
5.2 B-scan (a) showing the tip of the cone for aligning ultrasound therapy and imaging
transducers and the cone (b)……………………………………………………………..138
5.3 Experimental setup showing aligned therapy and imaging transducers positioned on
benchtop and on the animal liver………………………………………………………...139
5.4 Representative frequency spectrum in dB scale from one of the in vivo experiments
showing signal broadening at the fundamental (4 MHz) and first harmonic (8 MHz)
frequency…………………………………………………………………………………140
15 5.5 TTC stained image of tissue slice corresponding to the array imaging plane showing
ablated area and treated area margins. Thresholded black and white image of ablated area
(b) and treated area (c) for TTC stained section shown in Figure 5.5a………………….145
5.6 Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband
emission for an in vivo ultrasound ablation experiment (Trial 4)…………………..……147
5.7 Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband
emissions for in vivo ultrasound ablation experiment (Trial 5)………………………….147
5.8 Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband
emissions for in vivo ultrasound ablation experiment (Trial 6)……………………….....148
5.9 Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband
emissions for in vivo ultrasound ablation experiment (Trial 7)………………………….148
5.10 Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband
emissions for in vivo ultrasound ablation experiment (Trial 8)………………………….149
5.11 Spatially averaged time traces of fundamental, harmonic, subharmonic and broadband
emission levels for trials 4 (a) and 5 (b)…………………………………………………150
5.12 Spatially averaged time traces of fundamental, harmonic, subharmonic and broadband
emission levels for trials 6 (a) and 7 (b)…………………………………………………150
5.13 Spatially averaged time traces of fundamental, harmonic, subharmonic and broadband
emission levels for trial 8………………………………………………………………...152
5.14 Cropped sections of TTC stained tissue slices corresponding to the image plane for trials
4(a), 5(b), 6(c), 7(d) and 8(e) showing ablated and treated areas………………………..152
5.15 Traces of azimuthally resolved acoustic emissions (left) and corresponding lesion width
(right) from ablated and treated regions for trials 4 (top) and 5 (bottom)……………….153
16 5.16 Traces of azimuthally resolved acoustic emissions (left) and corresponding lesion width
(right) from ablated and treated regions for trials 6 (top) and 7 (bottom)……………….154
5.17 Traces of azimuthally resolved acoustic emissions (left) and corresponding lesion width
(right) from ablated and treated regions for trials 8……………………………………...154
5.18 ROC curves for classifying ablated and treated regions based on subharmonic and
broadband emissions……………………………………………………………………..156
5.19 ROC curves for classifying ablated and treated regions based on subharmonic and
broadband emissions……………………………………………………………………..158
17 Chapter 1
Introduction
Ultrasound can cause hyperthermia in tissue and hence can be used to cause cell necrosis in tumors and soft tissue. Ultrasound based thermal ablation techniques can prove to be an efficient curative treatment option for liver cancer. Bubble activity, including cavitation and tissue vaporization, have been shown to alter necrosis geometry and hence render ultrasound treatment unpredictable (Chen 2003) during high intensity focused ultrasound (HIFU). Typically ultrasound intensities of around 1000 W/cm2 and treatment duration on the order of a few seconds are employed in HIFU. Bubble activity can be monitored and manipulated to produce favorable ablation results. The role of bubble activity during bulk ultrasound ablation (intensity <
100 W/cm2 and treatment time of few minutes) has not been studied extensively yet.
Understanding the role of cavitation in bulk ultrasound ablation can make ablation safe and reliable.
The methods used in this thesis to evaluate the role of bubble activity in ex vivo and in vivo bulk ultrasound ablation are as follows. Overpressure was employed as a means to suppress bubble activity and evaluate the role of cavitation during ex vivo bulk ablation experiments.
Single element ultrasound transducers were used to passively monitor cavitation. Histological staining techniques were utilized to evaluate necrosis geometry. Passive cavitation imaging was performed using ultrasound array transducers for monitoring bubble activity in vivo. The results suggest that bubble activity plays a vital role in bulk ultrasound ablation and hence should be monitored to achieve better controlled treatments.
18 1.1 Background
To better understand bulk ultrasound ablation and its applications in the field of liver cancer treatment, related background information about liver cancer incidence, available treatment options and the extent of currently available knowledge on role of cavitation in ultrasound ablation are discussed below.
1.1.1 Liver cancer incidence
Liver cancer is the fifth most frequently diagnosed and second most frequent cause of death in men. Liver cancer is the seventh most commonly diagnosed and sixth frequent cause of death in women (Jemal et al. 2011). Liver and intrahepatic bile duct cancer is a major public health problem, with 24,120 new cases and 18,910 deaths estimated during 2010 (National Cancer
Institute website).
Liver cancer can be classified as primary or secondary liver cancer. When the cancer originates from the liver, it is termed primary liver cancer. Among primary liver cancer, hepatocellular carcinoma (HCC), accounting for 70% to 85% of total liver cancer, is the most prominent histological subtype. Hepatocellular carcinoma, also called malignant hepatoma, is the most frequent solid tumor occurring worldwide (Di Bisceglie 2009). Hepatitis B and C viruses increase the risk of liver cancer (Di Bisceglie 2009) with Hepatitis B virus accounting for 20% of liver cancers in developed countries. In United States and other low-risk regions, alcohol-related cirrhosis and obesity related fatty-liver disease are associated with the majority of liver cancer cases (Jemal et al. 2011).
Cancer that has spread to liver having originated in other organs such as colon, stomach, pancreas, breast and lungs is called metastatic or secondary liver cancer. Metastasis of primary colorectal tumors to liver is very common with an estimated 50,000 cases of colorectal liver
19 metastasis a year. It is estimated that only 5% to 10% of the patients with liver metastasis are candidates for curative liver resection (McCarter et al. 2000).
1.1.2 Liver cancer treatment options
Treatment options for liver cancer include surgical resection, cryosurgery (Onik et al. 1993,
Zhou et al. 1988, Zhang et al. 2011), percutaneous ethanol injection (PEI), electrolytic ablation
(Gravante et al. 2011), liver transplant and lately thermal ablation (Cabrera 2009).
1.1.2.1 Surgical resection
Surgical procedures are the choice of treatment for liver cancer for patients with good liver function. Surgical resection involves removing the tumor along with a tumor free margin to avoid tumor recurrence, leaving sufficient liver parenchyma to maintain normal liver function
(Hsieh 2011). Surgical resection has low five year survival rates of the order of 50% (Pawlik
2004) and does not prove to be effective on multiple tumors and extrahepatic diseases, which is often the case.
1.1.2.2 Percutaneous ethanol injection (PEI)
PEI involves intratumoral injection of absolute alcohol, resulting in coagulative necrosis of tumor cells. PEI is suitable for cirrhotic patients with a single small HCC nodule less than 3 cm
(Bartolozzi 1996). Although PEI is not associated with significant mortality and can spare normal tissue, implantation of HCC by needle track is not uncommon and can lead to tumor recurrence (Ishii 1998).
1.1.2.3 Electrolytic ablation
Electrolytic ablation involves applying a direct current (DC) across the tissue with gold or platinum electrodes. The current elicits different tissue responses depending on the voltage used.
20 High voltage DC heats up tissue causing electrocoagulation, whereas low voltage DC causes electrolysis in the tissue producing new electrical and chemical compounds, making the local microenvironment toxic and hence killing cells (Gravante 2011). Clinical use of electrolytic ablation is limited due to long treatment times for large tumors.
1.1.2.4 Cryosurgery
Cryosurgical ablation of liver tumor relies on tissue necrosis due to freezing and microvascular thrombosis. Argon-helium cryoablation induces formation of intracellular and extracellular ice crystals and cellular dehydration due to rapid freezing (< −140 °C) as well as rapidly thawing tissue (20-40 °C) with argon-helium gas. Three or less nodules 5 cm or less in diameter and irregular nodules > 5 cm in diameter can be treated with cryoablation (Zhang et al. 2011).
1.1.2.5 Liver transplantation
Liver transplantation is the replacement of diseased liver with a healthy liver allograft. The current guidelines for liver transplantation are based on the Milan criteria (Kim et al. 2006), which support transplantation for patients with one lesion ≤ 5 cm in diameter or up to three lesions ≤ 3 cm in diameter with associated 5-year survival rates of > 70% and tumor recurrence rate of 15%.
1.1.2.6 Thermal ablation
Excessive treatment duration in electrolytic ablation, lack of proper donor in transplants (Yao
2002) and low survival rates in surgical resection (Pawlick et al. 2004) are the major shortcomings of these methods that have led to alternative treatments for liver cancer. Multiple thermal ablation devices which use radiofrequency waves (Decadt 2004; Ni 2005), microwave
(Izzo 2003), and ultrasound (Mason 2011, Makin 2005) for tissue ablation are available. Sparing
21 normal tissue, lack of major side effects and available treatment monitoring options are the major advantages of thermal ablation over surgical resection.
Microwave ablation involves applying an electromagnetic field (900-2500 MHz) to the tissue.
The water molecules are forced to continuously align with the oscillating electric field which increases their kinetic energy and in turn heats the tissue. Microwave ablation has less susceptibility to vascular cooling and can be efficient for high impedance tissue like bone compared to RFA. Major disadvantages of microwave ablation include limited power capability due to cable heating and large diameter probes (Lubner 2010).
Radiofrequency ablation involves passing high frequency (> 10 kHz) current through the tissue with the use of electrodes. The alternating field displaces molecules in one direction then in the opposite direction, resulting in dielectric loss. Dielectric loss causes tissue heating which is the basis of radiofrequency ablation. By designing one electrode to be small, high current density can be maintained near the smaller electrode to produce focused ablation (Decadt 2004).
Monopolar radiofrequency ablation (RFA) has gained clinical acceptance as a curative option for liver tumors (Kudo 2004, Curley 2003, Zagoria 2003).
Poor tissue selectivity and perfusion losses, especially for tumors close to large blood vessels, may reduce the efficacy of RFA procedures. Contrast-enhanced CT, ultrasonography or MRI may be used to monitor treatment and assess necrosis. Gaseous vapor clouds produced during
RFA make it difficult to monitor thermal ablation using conventional diagnostic imaging methods (Goldberg 2000). Multiple insertions for larger tumors (Livraghi 2003), limited monitoring due to bubble formation (Goldberg et al. 2000) and poor control of ablation shape and size are some of the limitations of RFA, which have led the scientific community to investigate ultrasound ablation.
22 Ultrasound waves (frequency >20 kHz) can be absorbed by tissue and cause tissue hyperthermia. Ultrasound energy, focused to a tight spot in high intensity focused ultrasound
(HIFU) modality, can produce controlled millimeter size lesions. Weakly focused or unfocused ultrasound energy can be used to ablate larger centimeter size lesions. Dual mode ultrasound arrays capable of treatment and imaging can solve the problem of treatment monitoring.
1.1.3 Ultrasound ablation
1.1.3.1 Medical applications of ultrasound
Therapeutic uses of ultrasound include lithotripsy, histotripsy, transdermal drug delivery, thrombolysis and tumor therapy (Mason 2011). Shock wave lithotripsy involves applying high intensity ultrasound energy to produce shock waves at the focus which can break up kidney stones. In some clinical studies, 80% of the treatments were efficient in eliminating stones in kidney and ureter (Lima 2007, Halachmi 2006). Histotripsy, an extension of the lithotripsy technique utilizes pulsed focused ultrasound to produce mechanical destruction of tissue in a non-thermal, noninvasive manner (Roberts 2006, Xu 2005, Xu 2007). Ultrasound can be used for thrombolysis by either applying high intensity ultrasound sonication which mechanically disrupts the thrombus or lower intensity sonication which enhances enzymatic fibrinolysis (Francis 2001,
Datta et al. 2008). Ultrasound can cause a reversible increase in skin porosity and hence aid in transdermal drug delivery (Lai 2006, Maeda 2009 and Okada 2005).
Ultrasound energy can be focused to a tight spot using high intensity focused ultrasound
(HIFU) which has been shown to be efficient in treating tumors. HIFU treatment can be potentially delivered in an extracorporeal manner, resulting in a completely noninvasive therapeutic procedure (Kennedy 2004). HIFU, with typical intensities of the order of 1000
W/cm2 or higher and few seconds of treatment time, has great potential for treatment of liver
23 cancer (Kennedy 2003, Fukuda 2011), prostate cancer (Gelet 2000) and uterine fibroids (Stewart
2003). The small millimeter size lesions created with HIFU need to be overlapped with multiple treatments to cover a larger area. Bulk ultrasound ablation with intensities less than 100 W/cm2 and treatment duration of minutes can deliver unfocused ultrasound energy (Diederich 1999,
Chopra 2001, Makin 2005, Mast 2009) and cover larger treatment areas without having to mechanically scan the transducer. This approach can be used laparoscopically or interstitially and may provide an alternative to RFA (Makin 2005, Mast 2005, Mast 2009). Precise image- guidance methods will greatly improve the quality of ultrasound-based ablation approaches.
1.1.3.2 Bubble activity (cavitation and boiling) in ultrasound ablation
Bubble activity during ultrasound includes stable cavitation, inertial cavitation and tissue vaporization. Cavitation is the phenomenon of formation, oscillation and destruction of microscopic bubbles during the negative peak pressure cycle of the ultrasound wave. Stable cavitation involves bubbles oscillating around their equilibrium radii. Inertial cavitation occurs when the bubbles grow to an optimum size before imploding violently (Apfel 1981). When tissue is heated close to the boiling point of water, the fluid evaporates and causes tissue to vaporize. There are various methods to monitor or visualize these cavitation phenomena. An ultrasound transducer also called a passive cavitation detector (PCD) can be used to record acoustic signals associated with these cavitation phenomena. The signals can then be processed to pick out particular frequencies that indicate the presence of stable and inertial cavitation.
Stable cavitation produces signals at half frequency (f/2) of the fundamental frequency (f).
Inertial cavitation events involving sudden violent collapse of bubbles produce broadband signals. Tissue vaporization produces signals in the audible 10-20 kHz frequency range (Anand et al. 2004). It has been shown that half-order harmonics associated with stable cavitation events,
24 broadband signals associated with inertial cavitation and low-frequency signals associated with tissue vaporization can be passively monitored using a single element ultrasound transducer
(Mast 2008, Sanghvi 1995, Sapozhnik 2002, Hwang 2006, Madanshetty 1991). The single element PCD averages the received pressure field over its entire surface and hence loses spatial resolution. As an improvement to the PCD, an array transducer can be used to record these acoustic emission signals (Salgaonkar 2009a, Farny 2007, Gyöngy 2010). The signals can then be spatially resolved. A technique called passive cavitation imaging has been developed to monitor ablation in vivo (Salgaonkar 2009b).
1.1.3.3 Bioeffects of ultrasound
Cavitation effects (ter Haar 1981, Rabkin 2001) and tissue temperature rise have been shown to occur during ultrasound ablation. Bioeffects caused by temperature rise (thermal) and cavitation
(non-thermal) in ultrasound treatment are discussed below.
Thermal bioeffects
Bioeffects caused due to tissue temperature rise have been studied. It was recommended that temperature rise of 2 ºC or less for up to 50 hours does not cause significant biological effects, whereas temperature increases of 4 ºC and 6 ºC above normal for more than 16 min and 1 min respectively could cause bioeffects (O’Brien et al. 2008). An equation for calculating thermal dose, a measure of thermal effects produced for given tissue temperature (T in ºC) and the time of exposure (dt in minutes) is shown below (Merritt 1992).
Cumulative thermal dose =∑R (T-43) dt (1.1)
Here, T is the tissue temperature (ºC), dt is the time duration for which tissue was held at temperature T and the coefficient R is equal to 2 for T<43 ºC and 4 for T>43 ºC. The above equation suggests that tissue held at temperatures above 43 ºC even for a minute can produce
25 significant bioeffects. Tissue temperatures of 60 °C and above result in instant tissue coagulation
(Rempp 2011). Thermal effects of ultrasound ablation include tissue coagulation, protein denaturation, cessation of enzymatic activity and cellular necrosis (Mast 2009, Karunakaran
2008). Temperature elevations during ultrasound ablation can cause distinct concentric and measurable zones of thermal damage similar to those seen in Figure 2.3 with a central zone of ablation (tan), a zone of thermal coagulation (pink) and a peripheral viable zone. These zones of thermal damage produced along heat gradients are well described in Thomsen 2003.
Cavitation bioeffects
Cavitation has been known to occur during ultrasound ablation (ter Haar 1981, Hynyen 1991,
Coussios 2007, McLaughlan 2007, Melodelima 2004b and Mast 2005) and studies have looked at thresholds for cavitation in various tissues (Dunn 1975, Xu 2005).
Studies investigating bioeffects of cavitation have shown that cavitation may cause irreversible hind limb paralysis in mouse neonate at ultrasound intensities of 289 W/cm2 (Frizell
1983). Interaction of cavitation and ultrasound heating has been studied in vivo in mice and it was shown that cavitation can accelerate ultrasound lesion formation (Umemura 2005). Inertial cavitation has been shown to increase endothelial cell damage in blood vessels in vivo (Hwang
2006). Cavitation dose as calculated by temporal integration of broadband noise energy was shown to correlate with decreased cell viability in cell suspensions (Hallow 2006). Histotripsy, which relies on inertial cavitation, has been shown to produce controlled tissue erosion in rabbit kidney (Roberts 2006, Hall 2007).
Cavitation has been shown to cause enhanced ultrasound heating (Liu 2006, Biagi 2007,
Holt 2001), sonoporation (Lai 2006), thrombolysis (Datta 2008, Francis 2001), and produce larger lesions and higher lesion temperatures in vivo (Kaneko 2005, Sokka 2003). Chen 2003
26 reported that boiling at the HIFU focus produced gas bubbles that shielded ultrasound energy and led to tadpole shaped lesions that grew towards the transducer. Chavrier et al. (2000) modeled the formation of ultrasound induced lesions in the presence of cavitation. These studies confirm that cavitation during HIFU can complicate the treatment, rendering it unpredictable.
1.1.3.4 Role of cavitation in bulk ultrasound ablation
In all ultrasound based ablation techniques, cavitation can play a major role by interacting with thermal therapy. It is important to better understand the role of bubble activity in bulk ultrasound ablation in order to better monitor and control treatments and hence improve their efficacy and reliability. Some studies have looked at the effect of cavitation on bulk ultrasound ablation.
Melodelima et al. used a higher intensity (60 W/cm2) short pulse to produce bubbles before thermal treatment (14 W/cm2) and reported that a combination of cavitation and thermal US produced deeper lesions (Melodelima 2004b and 2004a).
Bubble activity during US treatments has been known to appear as hyperecho in the image
(Rabkin 2001, 2006). As shown by Mast 2008, onset of low-frequency tissue vaporization signals from bulk ultrasound ablation experiments correlated with an increase in mean grayscale value of B mode images. Modeling studies by Mast 2005 have simulated lesion production by image-ablate ultrasound arrays and show good agreement with experimental results when tissue vaporization and ablation-dependent attenuation were included in the model.
Overpressure can suppress cavitation by dissolving gas bubbles, restricting bubble oscillation and increasing the boiling point (Bailey 2001). HIFU ablation experiments performed at elevated pressure can produce less lesion distortion and larger lesion areas (Reed 2003). Maintaining ambient pressure such that instantaneous pressure is never less than vapor pressure assures cavitation suppression (Apfel 1981). Several models have been developed to explain the effect of
27 overpressure on cavitation activity (Khokhlova 2006a, Sapozhnik 2002). Experimental studies
(Bailey 2001, Khokhlova 2006a & 2006b, and Reed 2003) have used overpressure to suppress cavitation and hence study the role of cavitation in HIFU ablation.
1.1.3.5 Histology of ultrasound treated tissue
Treatment success for ultrasound tumor ablation can be evaluated based on the viability of the treated tumor, assessed by histologic stains such as hematoxylin & eosin (H&E), 4',6-diamidino-
2-phenylindole (DAPI) and triphenyl tetrazolium chloride (TTC) vital stains. Previous studies have employed H&E staining to indicate ultrasound-induced coagulative necrosis, evaluate cell viability and demarcate lesion boundary (Chen 1999). For bulk ultrasound ablation, where large tissue volumes are ablated at once, TTC staining techniques allow macroscopic assessment of tissue sections (Kinsey 2008, Bronskill 2007). Histologic changes corresponding to levels of
TTC uptake in TTC stained tissue has not been evaluated yet.
In this study, viability of rabbit liver and VX2 tumor tissue, treated by bulk ultrasound ablation was directly assessed from TTC uptake. Tissue viability was compared to corresponding hematoxylin and eosin (H&E) and 4',6-diamidino-2-phenylindole (DAPI) histology to understand the changes in cellular histology among regions with different TTC uptake levels.
1.2 Research Objectives
The thesis deals with evaluating the effects of cavitation and boiling on lesion formation during ultrasound bulk ablation. Effects of cavitation and boiling in HIFU have been studied by other groups and shown to render treatment unpredictable. Bubble activity, during bulk ultrasound ablation with lower intensities and longer treatment times, can behave differently than in HIFU and is hence important to study. Overpressure has been used as a method to suppress cavitation
28 activity and boiling and hence study the effect of cavitation on ablation geometry. Evaluation of ablated tissue was done using histologic techniques to provide insight on the mechanism of tissue necrosis. Similar experiments were performed in vivo, mapping cavitation activity and spatially correlating cavitation signals to necrosis histology, to obtain knowledge of cavitation mechanisms in vivo. The overall objective of the study was to understand the effect of bubble activity in bulk ultrasound ablation, which can later be used to control ablation.
1.3 Hypothesis and Specific Aims
The thesis aims at testing two major hypotheses. These hypotheses and the specific aims used to test them are stated below.
1. Thermal bioeffects as measured by thermal dose and cellular histology correlate to Triphenyl
Tetrazolium Chloride (TTC) uptake. i. To validate TTC staining as a means to evaluate degrees of tissue viability by comparing
TTC uptake to thermal dose for tissue heated by thermal conduction. ii. Quantify nuclear histology, as measured by DAPI, as a function of TTC uptake for liver
and tumor tissue treated by in vivo bulk ultrasound ablation.
2. Cavitation increases ablation effects by altering the thermal dose attained in tissue during ultrasound bulk ablation. i. To measure acoustic emissions and characterize lesion geometry from ex vivo bulk
ultrasound ablation performed with and without overpressure. ii. To develop statistical regression models to predict ablation geometry based on acoustic
emission levels for ex vivo ultrasound ablation. iii. To compare tissue histology among ultrasound treated and thermally heated tissues with
comparable thermal doses to further investigate role of cavitation in cell death.
29 iv. To map cavitation activity and compare to tissue histology for in vivo ultrasound ablation.
1.4 Thesis organization
The experimental methods and results to test the two hypotheses are presented in the chapters described below. The thesis has been organized into six chapters including this chapter titled
‘Introduction’.
Chapter 2, titled ‘Effect of overpressure on acoustic emissions and lesion histology in bulk ultrasound Ablation: Ex vivo’, describes the overpressure experiments conducted to suppress cavitation and hence evaluate the role of cavitation in altering lesion geometry in bulk ultrasound ablation (2.i and 2.ii). The chapter also describes statistical methods to predict lesion geometry based on recorded levels of cavitation activity.
Chapter 3, titled ‘Role of thermal and non-thermal mechanisms in tissue bioeffects for ex vivo bulk ultrasound ablation’, describes experiments comparing tissue histology between ultrasound treated and thermally heated tissues, with similar thermal doses, to evaluate the role of cavitation in altering thermal dose (2.iii). Chapter 3 also describes experiments designed to correlate TTC uptake to thermal dose (1.i).
Chapter 4, titled ‘Relations between nuclear histology and TTC uptake for liver and tumor tissue treated by bulk ultrasound ablation in vivo’, investigates the associations between thermal dose, TTC uptake and the tissue histology at different regions of TTC uptake (1.ii). The image processing algorithm used to quantify and compare DAPI nuclear histology of tissue from different TTC uptake regions is described in chapter 4.
Chapter 5, titled ‘Correlation of passive cavitation images and lesion histology for in vivo bulk ultrasound ablation’, describes experiments performed to monitor cavitation activity for in vivo bulk ultrasound ablation of swine liver (2.iv). The chapter deals with spatial correlation of
30 cavitation activity to lesion formation and hence provides a way to predict lesion formation based on acoustic emission levels. Receiver operating characteristic (ROC) analysis to predict lesion formation based on local acoustic emission levels is described in chapter 5.
The final ‘Conclusions and future work’ chapter deals with the strengths and limitation of the current study and discusses possible future research directions.
31 Chapter 2
Effect of Overpressure on Acoustic Emissions and Lesion Histology in Bulk Ultrasound Ablation: Ex Vivo
2.1 Objective
Understanding the role of cavitation in bulk ultrasound ablation can help us better control and monitor ultrasound treatment. Experiments presented in this chapter were designed to elucidate the role of cavitation and tissue vaporization in altering lesion geometry during ex vivo bulk ultrasound ablation. Pressure levels higher than the peak negative pressure of ultrasound
(overpressure) have been known to suppress cavitation and increase the tissue boiling point.
Hence, maintaining tissue under elevated pressure can be used as a method to evaluate the effect of cavitation and tissue vaporization in bulk ultrasound ablation. Use of MPa level overpressures to suppress bubble activity, including cavitation and tissue vaporization, has already been evaluated (Bailey 2001) for HIFU experiments. Studies have investigated the role of cavitation in
HIFU and found that presence of cavitation introduced unpredictability in the treatment (Chen
2003).
Fresh bovine liver tissue was ablated with ultrasound image-ablate arrays using unfocused continuous wave ultrasound at 31 W/cm2 (3.1 MHz and 4.8 MHz) for either 10 or 20 minutes.
The tissue chamber was maintained either at a static overpressure of either 0.52 MPa or 1.2 MPa to suppress tissue vaporization and (or) bubble activity, or at ambient pressure for control trials.
An overpressure of 1.2 MPa was needed to suppress cavitation at these ultrasound intensities. An
32 overpressure of 0.52 MPa was not sufficient to suppress cavitation but increased the tissue boiling point and hence helped elucidate the role of vaporization in bulk ultrasound ablation. A 1
MHz passive cavitation detector (PCD) recorded acoustic emission signals which were then used to quantify energy in the subharmonic (1.55 MHz or 2.4 MHz), broadband (1.2-1.5 MHz) and low-frequency (10-20 kHz) bins. Following ablation, the tissue was sliced and stained with 2% triphenyl tetrazolium chloride (TTC) solution to evaluate ablation geometry (Appendix).
Student t-tests were done to compare acoustic emission levels and lesion geometry among experiments performed at different levels of overpressure. Cross-correlation coefficients were computed to identify any associations between acoustic emissions and lesion geometry.
Multivariate multiple regression models were created to predict lesion geometry based on the acoustic emission levels.
2.2 Materials and methods
To evaluate the role of bubble activity and vaporization, bulk ultrasound ablation experiments were performed under elevated pressure to suppress bubble activity, or under ambient pressure for control experiments. It has been shown that one way to suppress cavitation activity is to maintain the ambient pressure higher than the peak negative pressure (Apfel 1981). The higher ambient pressure may obstruct the growth of small bubbles and hence prevent them from cavitating. An overpressure chamber was designed that could withstand up to 200 psi (1.3 MPa) of pressure and was used to suppress bubble activity.
2.2.1 Design of overpressure chamber
A polyethylene terepthalate (PET) cylindrical bottle 83 mm long, 32 mm diameter, and 0.78 mm wall thickness (Container and Packaging supply, Eagle, ID) was used as the pressure chamber.
Based on the bottle material and dimensions, it was calculated that the bottle can withstand radial
33 pressure of about 1.3 MPa based on Barlow’s equation for pipe burst pressures (Oberg 1942).
Barlow’s equation for calculating burst pressure (P) of a pipe is given by equation 2.1.
P = 2 s t , (2.1) do SF where P is the maximum working pressure in MPa, s is the material strength in MPa, t is the wall thickness of the bottle in mm, do is the outside diameter of the bottle in mm, and SF is the safety factor which ranges from 1.5 to 10. For the PET bottle used in these experiments, with material strength of 55 MPa, wall thickness of 0.78 mm and diameter of 32 mm, a working pressure of
1.3 MPa was calculated for a safety factor of 2.
2.2.2 Acoustic properties of the chamber
Since ultrasound was transmitted through the bottle wall to ablate tissue inside the chamber, it was important to estimate acoustic properties of the PET material. A simple acoustic power transmission experiment was done on the bottle using a radiation force balance. Ultrasound energy was transmitted through the wall of the bottle and the transmitted power was measured on the other side using a radiation force balance (Ohmic Instruments Co., Easton, MD).
Transmission coefficients of 0.8553 and 0.7745 were measured at 3.5 MHz and 4.8 MHz respectively. A three layer problem which accounts for complex sound speed (Kinsler and Frey
1982), with the PET bottle as the middle layer and water as two surrounding layers, was solved based on equation 2.2.
2 2z z eik pt T p w 2 2 2 (2.2) z p zw cosk pt iz p zw sink pt
In equation 2.2, T is the fraction of ultrasound power transmitted through the PET layer. kp is
a complex number equal to iA , where ω = 2πf, f is the ultrasound frequency, cp is the sound c p
34 speed in PET material (1.95 mm/µs) and A is the attenuation of the medium in Nepers/mm.
Attenuation A is calculated as 20∙log10 (α), where α is the attenuation coefficient in dB/mm at
frequency f. Complex speed of sound in PET (cp) is calculated as . PET acoustic impedance k p
3 (zp) is calculated as a product of PET material density (0.9 kg/m ) and cp. In equation 2.2, t is the
PET layer thickness (0.78 mm) and zw is the acoustic impedance of water at normal incidence, equal to the product of water density (0.998 kg/m3) and speed of sound (1482 m/s) in water.
Transmission coefficients were calculated for attenuation coefficient (α) values ranging from
0 to 40 dB/mm at 3.5 MHz and 4.8 MHz. Attenuation (A) values which yielded calculated transmission coefficients matching measured transmission coefficients at the above two frequencies were identified (Figure 2.1).
Ultrasound attenuation of PET (0.78 mm wall thickness) was found to be 1.07 dB at 4.8 MHz and 0.62 dB at 3.5 MHz (Figure 2.1). The PET ultrasound attenuation coefficient was calculated as 2.28 dB/(cm∙MHz) and 2.85 dB/(cm∙MHz) at 3.5 MHz and 4.8 MHz respectively. The averaged ultrasound attenuation coefficient of PET was calculated to be 2.56 dB/(cm∙MHz). The same three layer problem (equation 2.2) was solved with the calculated PET ultrasound attenuation coefficient [2.56 dB/(cm∙MHz)] in the equation to obtain extrapolated power transmission coefficients of 84% and 79% at 3.1 MHz and 4.8 MHz respectively. Figure 2.2 shows the calculated transmission coefficients at 3.1 MHz and 4.8 MHz for varying wall thickness values. The calculated transmission coefficients were used to plan input radiofrequency power to the transducer such that in situ ultrasound intensity of 31 W/cm2 (0.9 MPa) could be obtained.
35 1 a 0.9
0.8 X: 0.8 Y: 0.8559 0.7
0.6
0.5
coefficient 0.4 Power Power transmission 0.3
0.2
0.1
0 0 5 10 15 20 25 30 35 40 Attenuation coefficient (dB/mm)
1
0.9 X: 1.37 Y: 0.7744 b 0.8
0.7
0.6
0.5
coefficient 0.4 Power Power transmission 0.3
0.2
0.1
0 0 5 10 15 20 25 30 35 40 Attenuation coefficient (dB/mm)
Figure 2.1: Power transmission coefficient (equation 2.2) of the PET bottle for a range of attenuation coefficients at 3.5 MHz (a) and 4.8 MHz (b). The cursors indicate the attenuation coefficients for which theoretical transmission coefficients matched the measured values.
36 T 1 a 0.9
0.8
0.7
0.6 Power transmission Energy transmission 0.5 Energy reflection
Coefficient 0.4
0.3
0.2
0.1
0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Thickness (mm)
T 1 b 0.9 0.8
0.7
0.6 Power transmission 0.5 Energy transmission Energy reflection Coefficient 0.4
0.3
0.2
0.1
0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Thickness (mm)
Figure 2.2: Reflection and transmission coefficients for the PET bottle at 3.1 MHz (a) and 4.8 MHz (b) for range of wall thickness values.
For construction of the pressure chamber, the bottle was threaded onto galvanized iron pipe fittings and in turn connected to a hydraulic hand pump (Ralston Instruments, Inc.). Figure 2.3a shows the overpressure chamber assembly. The overpressure assembly was tested several times by pressurizing it to 175 psi (1.2 MPa) and inspected for pressure leaks.
37 2.2.3 Ultrasound image-treat probes
Ultrasound array probes comprising 32 PZT-4 elements with air backing, similar to probes used in earlier studies (Mast et al. 2005, 2008, 2009, 2011, Makin 2005, Karunakaran et al. 2008) were used for ablation. These probes (Figure 2.3a) are capable of performing pulse-echo imaging and continuous-wave therapy using the same elements. The two probes used in this study (THX
3N and THX 4A, Guided Therapy Systems, Mesa, AZ) are identical except for their operating frequencies, element pitch, and array dimensions. The THX 3N is a 32 element, 2.3∙49 mm2 probe operating at 3.1 MHz. The THX 4A is a 32 element, 2.3∙20 mm2 probe operating at 4.8
MHz. Acoustic power measurements were done using a radiation force balance (Ohmic
Instruments Co., Easton, MD). Acoustic power output from the transducer as a function of input radiofrequency power was recorded and later used to plan treatment settings (Makin et al. 2005).
The Iris ultrasound therapy system (Guided Therapy Systems, Mesa, AZ) shown in Figure
2.3b consists broadly of a control/interface module (Figure 2.3c), an array imaging module and an array therapy module. The control/interface module in turn consists of a host PC running the
Iris software program that interfaces to the array imaging and array therapy modules (Figure
2.3c). The control/interface module can be used to set treatment parameters including input voltage to the ultrasound array, duration of treatment and the rate at which B-mode images are to be saved during the treatment. The image-treat ultrasound probes connect to the array therapy module (Barthe et al. 2004). The Iris software can capture live images interspersed within treatment pulses. For the experiments described here, images were recorded every 2.6 seconds.
38 Ultrasound PCD Pressure b a array chamber
c
Figure 2.3: Overpressure experimental setup. (a) Overpressure chamber assembly showing the ultrasound array, the passive cavitation detector and the pressure chamber. (b) Iris system with array imaging and therapy module and the host computer. (c) Block diagram of driving electronics for the Iris system.
39
2.2.4 Tissue preparation and handling
All experiments used slaughterhouse-derived excised bovine liver that was treated within 12 hours post-mortem. Tissue was transported in a bag of ice-cold phosphate buffered saline (PBS) placed inside an insulating container. It was then cut into 80∙35∙30 mm3 pieces to fit the pressure chamber and placed in cold PBS, to avoid tissue degradation and ensure that the initial tissue temperature for all experiments remained comparable.
2.2.5 Experimental setup
Figure 2.3a shows the setup for these experiments. Liver tissue was placed into the pressure chamber, after which the chamber was filled with degassed saline. The chamber was then sealed, pressurized and placed in the water tank at a distance of 25 mm from the ultrasound array surface, with the liver capsule facing the active array surface. Two levels of overpressure (atmospheric pressure and 1.2 MPa) were used for the 3.1 MHz experiments.
Overpressure of 1.2 MPa was required to suppress cavitation activity for ultrasound peak pressures of 0.92 MPa corresponding to ultrasound intensity of 31 W/cm2. Two levels of overpressure, 0.52 MPa and 1.2 MPa, in addition to atmospheric pressure were used for the 4.8
MHz experiments. 1.2 MPa of pressure suppresses cavitation activity and increases the water boiling point to 189 ºC. The intermediate level of overpressure (0.52 MPa) was not sufficient to suppress cavitation activity and moderately increased the water boiling point to 153 ºC. The intermediate level of pressure in the 4.8 MHz experiments allowed for evaluation of the effect of overpressure on tissue vaporization and hence on ablation results. The atmospheric pressure level served as a control condition.
The ultrasound image-treat arrays (4.8 MHz or 3.1 MHz) sonicated tissue with unfocused ultrasound at 31 W/cm2 intensity for 10 or 20 minutes respectively. A 1 MHz unfocused
40 broadband transducer used as a passive cavitation detector (PCD) (C302, Panametrics, Waltham,
MA, USA) was placed perpendicular to the array at a distance of 15-20 mm facing the tissue chamber.
2.2.6 Ultrasound ablation
The first set of bulk ultrasound ablation experiments used a 3.1 MHz, 3 mm diameter, 32- element array with an active area of 2.3∙49 mm2 (THX 3N). The center sixteen elements were set to fire at 31 W/cm2 ultrasound intensity for 20 minutes.
A second series of bulk ultrasound ablation experiments employed a 4.8 MHz, 3-mm diameter, 32-element array with an active area of 2.3∙20 mm2 (THX 4A) All 32 elements were fired at 31 W/cm2 for duration of 10 minutes. The higher tissue attenuation at 4.8 MHz caused tissue vaporization to occur typically within 10 minutes and hence the treatment duration was reduced to 10 minutes as opposed to 20 minutes for 3.1 MHz experiments.
All 4.8 MHz experiments included two thermocouples placed into the tissue to record tissue temperature. Two 33-gauge (0.2 mm diameter), type K bare junction thermocouples (Ella CS,
Hradec Králové, Czech Republic) were inserted into the tissue. One of the thermocouples was placed approximately 10 mm from the liver capsule surface (≈ 25 mm from the array surface) and at a depth of 25 mm, where tissue temperature during the treatment was known to reach about 70-100 ºC. The other thermocouple was placed at approximately 20 mm from the liver capsule (≈ 35 mm from the array surface) and 50 mm deep into the tissue where tissue temperature during the treatment was known to reach around 50-70 ºC. Actual positions of the thermocouples in the tissue were recorded on the B-mode images and later marked while slicing the tissue post treatment. Figure 2.4 shows the position of the two thermocouples (white markers) in different thermal dose regions.
41
Thermocouple 1 Depth
Ultrasound Thermocouple 2 array Direction of Ultrasound
Figure 2.4: Image of ultrasound treated tissue showing thermocouple positions (white markers) and associated needle tracks. 2.2.7 Data acquisition
Time signals captured by the PCD were band-pass filtered between 3 kHz to 1 MHz with a 6 dB/octave roll-off rate and amplified by a low-noise amplifier, with an amplitude gain of 200 for the 3.1 MHz and 1000 for the 4.8 MHz experiments. The PCD signals were band-pass filtered, amplified through a low noise amplifier (SR 560, Stanford Research Systems, Elgin, IL) and recorded once every 2.6 s by a digital oscilloscope (Waverunner, Lecroy, Chestnut Ridge, NY) for frequency analysis. Signals of 1 s duration were recorded once every 2.6 seconds at sampling rates of 10 MHz for the 3.1 MHz trials and 25 MHz for the 4.8 MHz trials. B-mode images of the tissue before and after ablation were acquired and stored through the Iris software. A temperature data logger (Omegaette HH306, Omega Engineering, Stanford, CT, USA) recorded the two thermocouple readings once every 2 seconds.
2.2.8 Histology evaluation
Following ablation, the tissue was sliced along the image/treat plane and the positions of the two thermocouples were photographed for visual confirmation (Figure 2.4). Tissue sections were stained with a 2% (w/v) TTC solution for 45 minutes (Appendix). The stained sections were then
42 scanned on a flatbed scanner (CanonScan 8800F) at 300 dpi resolution. Ablation geometry after
TTC staining is shown in Figure 2.5. An inner bleached region was observed with no TTC uptake due to cessation of enzymatic activity, which is considered nonviable or ablated.
Surrounding the ablated area is a region staining pink due to reduced metabolic activity resulting in partial TTC uptake which is considered partially viable. Normal untreated liver takes up TTC completely, causing a dark red appearance, and is considered viable. The ablated area (AA), treated area (TA), ablated depth (AD) and treated depth (TD) were marked and measured using
ImageJ software (National Institute of Health) as shown in Figure 2.5. Tissue regions with no or partial TTC uptake were characterized as treated and regions with no TTC uptake were characterized as ablated. Partial TTC uptake Thermocouple Complete TTC uptake (Partially viable) location (Viable)
Treated depth No TTC uptake (Nonviable) Ablated depth
Direction of ultrasound
Figure 2.5: TTC stained section of ultrasound treated tissue showing ablated (AA) and treated areas (TA), ablated (AD) and treated depths (TD) and thermocouple location.
2.3 Data processing
Instantaneous power spectra of the PCD signals were calculated by the periodogram method using 2000 point FFTs for the 3.1 MHz trials and 5000 point FFTs for the 4.8 MHz trials, with
0.2 ms long rectangular windowing and 5000 averages over the 1 s signal duration. For each trial,
43 acoustic emission levels were quantified as the time-averaged spectral energy within three distinct frequency bands: subharmonic (SH - 1.55 MHz or 2.4 MHz), broadband (BB - 1.2 to 1.5
MHz), and low-frequency (LF - 5 to 20 kHz) throughout the experiment.
Emission levels were quantified by temporally averaging the energy in each emission band and then converting it to a decibel level relative to the energy in the same emission band from the mean baseline noise floor spectrum. The mean baseline noise floor spectrum was calculated by averaging the baseline noise floor during the initial 30 s of the treatment when the ultrasound was off from all 22 or 18 trials for the 3.1 MHz or 4.8 MHz experiments respectively.
While displaying the time-frequency spectrum plots (Figure 2.7 & Figure 2.10), baseline dB leveled spectral energy has been subtracted from the instantaneous dB leveled spectral energy to show the relative increase in spectral energy during the treatment.
2.3.1 Spectral leakage processing
For the 4.8 MHz experiments, it was noticed that there was broadband energy leakage into the low-frequency band. Modifications were made to the data processing algorithm to reduce spectral leakage and enhance the detection of low-frequency signals. Average energy in the broadband frequency band was subtracted from average energy in the low-frequency band to correct for this spectral leakage. Acoustic signatures associated with inertial cavitation that occur as a broadband phenomenon were reduced for frequencies greater than 1 MHz due to the use of a band-pass filter, and hence no spectral leakage was noticed at the subharmonic frequency bins
(1.55 MHz or 2.4 MHz). Energy in the four bins covering 1.2 to 1.215 MHz was averaged to estimate the broadband energy BB(t), where t is time. Similar averaging was done for the four bins covering 5 to 20 MHz to obtain an estimate for the low-frequency energy LF(t). A sensitivity factor S was calculated as the mean ratio of averaged low-frequency energy to
44 averaged broadband energy for the first ten recorded data points in the experiment, when there was no tissue vaporization. The post-processed low-frequency signal LFp (t) is calculated by equation 2.3.
LFp (t) = LF (t) − S∙BB (t) + LFbase (2.3)
The time dependent post-processed low-frequency signal was averaged over the ultrasound exposure duration and converted to dB with respect to the low-frequency bin baseline LFbase. The low-frequency bin baseline (LFbase) was calculated by averaging low-frequency energy for data points from the initial 30 s of treatment when the ultrasound was off. The addition of the low- frequency baseline term LFbase to the equation ensures that quantified decibel level values are scaled with respect to the baseline.
An example showing the effect of spectral leakage processing is shown in Figure 2.6. Figure
2.6a shows the original broadband and low-frequency signals for one of the 4.8 MHz control experiments. It is seen that the low frequency signal closely resembles the broadband signal due to spectral leakage. Figure 2.6b shows the post-processed low-frequency signal with the onset of low frequency activity at approximately four minutes and clearly evident peaks.
45 95 Low-frequency a Broadband
90
85
80 Emisson energy (dB) Emisson energy
75
70 1 2 3 4 5 6 7 8 9 10 Treatment time (minutes)
15 b
10
5
0
-5 Emission energy Emission (dB) energy -10
-15
-20 1 2 3 4 5 6 7 8 9 10 Treatment time (minutes)
Figure 2.6: Representative original and post processed low-frequency signals from a 4.8 MHz experiment showing trace of low-frequency and broadband signal with spectral leakage (a) and post-processed low-frequency signal (b).
46 2.4 Statistical analysis
Student t-tests were performed on lesion geometry metrics (AA, TA, AD, TD) and acoustic emission levels (SH, BB, LF) from all experimental groups to determine any statistically significant differences.
Cross-correlation coefficients were computed between the ablation (AA, TA, AD, TD) and emission (SH, BB, LF) results to determine any significant correlations between the two variables.
A correlation analysis verifies the linear association between two variables only. Multivariate multiple regression analysis on the other hand can verify association of one or more independent variables to one or more dependent variables. A justification for performing multivariate regression is that the response variables AA, TA, AD and TD are all correlated and there is a need to adjust the regression coefficients and p-values taking into account the correlation. Hence, multivariate multiple regression analysis can shed light on the relative contributions of each acoustic emission in predicting bulk ablation results and thus provide information on effectiveness of monitoring acoustic emissions to predict or control ablation results.
Multivariate multiple regression modeling was done with the three emission levels i.e. subharmonic (SH), broadband (BB) and low-frequency (LF) as inputs to the model to predict lesion areas and depths (AA, TA, AD, and TD). To compare the regression coefficients with each other, all the emission levels, lesion areas and depths were standardized by subtracting the mean of the data group from each observation and then scaling it by the standard deviation of the group. Each of the four outputs, AA, AD, TA, and TD was predicted based on the inputs as shown in equation 2.4.
Y= B0 + B1∙ (SH) + B2 ∙ (BB) + B3 ∙ (LF) + B4 ∙ (Var1.2) + B5 ∙ (Var0.52) (2.4)
47 B1, B2 and B3 are the regression coefficients corresponding to the three inputs SH, BB and LF respectively. To test the effect of overpressure conditions on the output, two more regression coefficients B4 and B5 associated with categorical variables Var1.2 (1.2 MPa versus control) and
Var0.52 (0.52 MPa versus control) respectively were included in the equation. B4 and B5 test for significance of the effect of each overpressure condition on the output with respect to control as baseline. For the 4.8 MHz experiments, with two levels of overpressure being compared to control, two regression coefficients (B4 and B5), in addition to B1, B2 and B3 are needed. For the
3.1 MHz experiments with only one level of overpressure to compare to control, only one additional regression coefficient (B4) is needed.
It was noted that B4 and B5 did not help predict lesion geometry, confirming that for the same levels of acoustic emissions across the three different overpressure experiments, overpressure by itself could not predict lesion geometry. Hence, B4 and B5 were later dropped out of the equation to improve prediction effectiveness and the multivariate model was simplified to equation 2.5.
Y= B0 + B1 ∙ (SH) + B2 ∙ (BB) + B3 ∙ (LF) (2.5) 2.5 Results
2.5.1 Experimental results at 3.1 MHz
Representative PCD spectrum plots (Figure 2.7) and individual emission traces (Figure 2.8) are shown for control and 1.2 MPa experiments. It is seen from that the control experiment had some subharmonic peaks and evident low-frequency emissions, whereas the 1.2 MPa experiment had no substantial subharmonic or low-frequency emissions.
48 a
b
Figure 2.7: Representative spectra showing subharmonic, broadband and low-frequency emission levels (dB) for control (a) and 1.2 MPa (b) experiments performed at 3.1 MHz.
49 dB-scaled PCD emissions, trial 1, date 8608, run t2 10 a 5
Subharmonic 0 0 5 10 15 20 2
1
0 Broadband
0 5 10 15 20 10
5
0 Low-Frequency 0 5 10 15 20 Treatment time (min)
dB-scaled PCD emissions, trial 2, date 8608, run t3 10 b 5
Subharmonic 0 0 5 10 15 20 2
1
0 Broadband
0 5 10 15 20 10
5
0 Low-Frequency 0 5 10 15 20 Treatment time (min)
Figure 2.8: Representative acoustic emission traces showing subharmonic, broadband and post- processed low-frequency emission levels (dB) for control (a) and 1.2 MPa (b) experiments performed at 3.1 MHz.
50 The three acoustic emission levels were compared across the 11 control and 11 1.2 MPa trials.
A summary of average emission levels from the two experimental groups is shown in Figure 2.9.
The mean subharmonic level decreased from 0.26±0.21 dB for the eleven control trials to
0.05±0.06 dB for the eleven 1.2 MPa trials.
1 1.2 MPa Control 0.8
0.6
0.4
Mean Emission Level(dB) Emission Mean 0.2
0 Subharmonic Broadband Low-frequency
Figure 2.9: Comparison of averaged subharmonic, broadband and low-frequency emission levels among control and overpressure (1.2 MPa) runs for the 3.1 MHz experiments.
Figure 2.10 shows representative TTC stained sections from control and 1.2 MPa trials. It can be seen from the images that the 1.2 MPa experiment resulted in a smaller ablated area than the control trial. Figure 2.11 shows a summary of lesion geometries from the two experimental groups. Mean ablated area decreased from 1.65±0.85 cm2 for the eleven control trials to
0.65±0.70 cm2 for the eleven 1.2 MPa trials. Table 2.1 displays the Student t-test results for comparing emission and ablation results from 1.2 MPa trials with corresponding variables from control trials as reference. Table 2.1 shows that trials performed under 1.2 MPa of pressure resulted in smaller ablated areas (t = −3.0276, p = 0.0066) and reduced levels of subharmonic emissions (t = −3.1512, p = 0.0050). These results suggest that overpressure results in reduced lesion areas and reduced cavitation activity.
51 Lesion areas and depths were correlated to emission levels and the results are shown in Table
2.2. Table 2.2 shows correlation coefficients (r) and corresponding probability values (p) for 22 runs performed at 3.1 MHz. Correlations between particular acoustic emissions and lesion geometries have been highlighted if statistically significant (p ≤ 0.05). Subharmonic emission levels correlated positively with ablated area (r = 0.5592) and treated area (r = 0.5729).
Broadband emissions correlated positively with ablated area (r = 0.4402), treated area (r =
0.4656) and ablated depth (r = 0.6106). Low-frequency emission levels correlated positively with ablated area (r = 0.53).
Control 1.2 MPa
Figure 2.10: Representative TTC stained sections of tissue treated under control (left) and 1.2 MPa (right) conditions at 3.1 MHz.
52 4.5 a 1.2 MPa 4 Control
3.5
3 ) 2 2.5
2 Area (cmArea 1.5
1
0.5
0 Ablated Treated
b 2 1.2 MPa 1.8 Control 1.6
1.4
1.2
1
Depth (cm) Depth 0.8
0.6
0.4
0.2
0 Ablated Treated
Figure 2.11: Comparison of treatment areas (a) and depths (b) between control and overpressure (1.2 MPa) runs at 3.1 MHz.
53 SH BB LF AA TA AD TD t = −3.1512 t = −0.7209 t = −1.1719 t = −3.0276 t = −1.3893 t = −1.4705 t = 0.5917 p = 0.0050 p = 0.4793 p = 0.2550 p = 0.0066 p = 0.1800 p = 0.157 p = 0.5607
Table 2.1: t statistics and p values for comparing acoustic emissions and treatment geometry across 1.2 MPa and control trials in 3.1 MHz experiments.
SH BB LF AA TA AD TD SH 1
r = −0.3354 BB p = 0.1271 1
r = 5848 r = 0.2394 LF p = 0.0043 p = 0.2833 1
r = 0.5592 r = 0.4402 r = 0.53 AA p = 0.0068 p = 0.0403 p = 0.0112 1
r = 0.5729 r = 0.4656 r = 0.3938 r = 0.7214 TA p = 0.0053 p = 0.029 p = 0.0663 p = 0.0002 1
r = 0.3807 r = 0.6106 r = 0.2818 r = 0.8183 r = 0.6027 AD p = 0.0805 p = 0.0025 p = 0.2038 p = 0 p = 0.003 1
r = 0.0779 r = 0.3134 r = 0.0105 r = 0.4819 r = 0.6034 r = 0.5244 TD p = 0.7305 p = 0.1555 p = 0.9631 p = 0.0231 p = 0.0029 p = 0.0122 1
Table 2.2: Correlation coefficients (r) and corresponding probability (p) values for correlating acoustic emissions (SH, BB and LF) levels to treatment geometry (AA, TA, AD and TD) for 3.1 MHz experiments. Significant correlations (p <0.05) are highlighted.
Table 2.3 shows the multivariate multiple regression results for the 3.1 MHz experiments based on equation 2.4, with B4, the regression coefficient corresponding to 1.2 MPa overpressure conditions (Var1.2). It is seen from Table 2.3, that the overpressure conditions Var1.2 had no significant regression to any of the outputs and hence it was decided to drop B4 from the model.
Table 2.4 shows the multivariate multiple regression results for the 3.1 MHz experiments based on equation 2.5, after dropping B4 and B5. The table shows regression coefficients B1, B2 and B3, corresponding to subharmonic (SH), broadband (BB) and low-frequency (LF) emission levels, in
54 predicting the outputs, ablated area (AA), treated area (TA), ablated depth (AD) and treated depth (TD). The last two columns contain p-values and R2 values for the model. R2 values signify the proportion of variance in output that is explained by the inputs in the model and is a measure of how well the model predicts the output.
2 Intercept SH (B1) BB (B2) LF (B3) V1.2 (B4) p R (B0) AA −0.4105 0.0512 0.1500 0.3434 0.8210 0.0147 38.12% (P=0.1426) (P=0.8488) (P=0.444) (P=0.1280) (P=0.0647) TA 0.0124 0.4466 0.2229 0.0657 −0.0248 0.0783 37.36% (P=0.967) (P=0.150) (P=0.312) (P=0.787) (P=0.958) AD −0.2000 0.0359 0.4549 0.0569 0.4001 0.1252 33.1% (P=0.5254) (P=0.9078) (P=0.0555) (P=0.8212) (P=0.4163) TD 0.2613 0.2665 0.0336 −0.0889 −0.5226 0.9029 5.651 % (P=0.485) (P=0.473) (P=0.900) (P=0.766) (P=0.372)
Table 2.3: Multivariate multiple regression coefficients (B), probability (p) and total variance explained (R2) for predicting AA, TA, AD and TD in 3.1 MHz experiments, based on equation 2.4. Significant p values have been highlighted.
2 SH (B1) BB (B2) LF (B3) p R AA 0.3372 0.1128 0.2950 0.02994 38.41% (p=0.175) (p=0.589) (p=0.215) TA 0.4379 0.224 0.0672 0.0345 37.35% (p=0.0856) (p=0.2937) (p=0.7751) AD 0.1753 0.4368 0.0333 0.0826 30.37% (p=0.4985) (p=0.0608) (p=0.8930) TD 0.0845 0.0573 −0.0582 0.9801 0.990 % (p=0.783) (p=0.828) (p=0.844)
Table 2.4: Multivariate multiple regression coefficients (B), probability (p) and total variance explained (R2) for predicting AA, TA, AD and TD in 3.1 MHz experiments, based on equation 2.5. Significant p values have been highlighted.
It is seen from p-values in the second to last column in Table 2.4, that the model was statistically significant in predicting ablated area and treated area only. In addition, it is also seen that none of the other p-values corresponding to the individual inputs were significant. The fact that the individual regression coefficients and the overall model for AD and TD were not significant suggests that none of the inputs, individually or combined, could predict AD and TD for the 3.1 MHz experiments with statistical confidence. Also, considering the low R2 values, it
55 is seen that the models were not very reliable in predicting the outputs based on the given input variables.
Comparing p-values for the three acoustic emissions for predicting each output, it can be seen that the subharmonic emissions may be the most significant in predicting ablated area. For predicting ablated depth, broadband emission levels had p-values marginally higher than 0.05 and may be more significant than the other inputs.
Comparing the p-values for a single acoustic emission input in predicting the four outputs, it can be seen that subharmonic levels were most significant in predicting treated areas. Broadband levels were most significant in predicting ablated depth. Low frequency emission levels were most significant in predicting ablated area.
2.5.2 Experimental results at 4.8 MHz
Figure 2.12 shows plots of time dependent emission spectra for representative control, 0.52 MPa and 1.2 MPa runs at 4.8 MHz. Figure 2.13 shows corresponding averaged emission and temperature traces for representative control, 0.52 MPa and 1.2 MPa runs from 4.8 MHz experiments. For the control trial shown, there were substantial subharmonic and broadband events, evident by the peaks at 2.4 MHz and the rise in broadband levels respectively. Tissue vaporization, indicated by low-frequency emissions, occurred about 3 minutes after the start of the treatment. For the 0.52 MPa overpressure trial, where cavitation activity should not be suppressed, but the boiling point was raised to 153 ºC, the onset of low-frequency emissions occurred later in the treatment at about 6 minutes. The 0.52 MPa overpressure trial had subharmonic and broadband emission levels lower than the control trial but higher than the 1.2
MPa trial. The 1.2 MPa overpressure trial had suppressed subharmonic and broadband activity and no tissue vaporization.
56 Figure 2.14 summarizes acoustic emissions levels from all three experimental groups. The bar graphs show that subharmonic and low-frequency emission levels decreased with increasing overpressure. The mean subharmonic emission level decreased from 5.42±2.39 dB for six control trials to 2.90±1.80 dB for the six 0.52 MPa trials and 2.01±1.07 dB for the six 1.2 MPa trials. Mean low-frequency emission levels decreased from 3.93±2.82 dB for the six control trials to 1.37±2.82 dB for the six 0.52 MPa trials and 0.51±0.70 dB for the six 1.2 MPa trials. However, broadband emission levels showed no evident trend with increasing overpressure.
57 Control a
b 0.52 MPa
c 1.2 MPa
Figure 2.12: Representative time spectrum plots for control (a), 0.52 MPa (b) and 1.2 MPa (c) trials at 4.8 MHz
58 dB-level PCD emissions and temperature (C), trial 21, date 10809, run 8 15 a 10 5 Control 0 Subharmonic 0 2 4 6 8 10 15 10 5
Broadband 0 0 2 4 6 8 10 30 20 10 0
Low-Frequency 0 2 4 6 8 10 150 100 50
Temperature 0 0 2 4 6 8 T1 10 Treatment time (min) T2
dB-level PCD emissions and temperature (C), trial 15, date 102209, run 2 15 b 10 5 0 0.52 MPa Subharmonic 0 2 4 6 8 10 15 10 5
Broadband 0 0 2 4 6 8 10 30 20 10 0
Low-Frequency 0 2 4 6 8 10 150 100 50
Temperature 0 0 2 4 6 8 T1 10 Treatment time (min) T2
dB-level PCD emissions and temperature (C), trial 10, date 102009, run 4 15 10 c 5 0 1.2 MPa Subharmonic 0 2 4 6 8 10 15 10 5
Broadband 0 0 2 4 6 8 10 30 20 10 0
Low-Frequency 0 2 4 6 8 10 150 100 50
Temperature 0 0 2 4 6 8 10 T1 Treatment time (min) T2 Figure 2.13: Individual emission (subharmonic, broadband, post-processed low-frequency) and temperature traces from representative control (a), 0.52 MPa (b) and1.2 MPa (c) trials at 4.8 MHz.
59
1.2 MPa 0.52 MPa Control 15 14 13 12 11 10 9 8 7 6 5 Mean Emission Level(dB) Emission Mean 4 3 2 1 0 Subharmonic Broadband Low-frequency
Figure 2.14: Comparison of averaged acoustic emission (subharmonic, broadband and low- frequency) levels for control, 0.52 MPa and 1.2 MPa runs at 4.8 MHz.
Figure 2.15 shows temperature traces from control, 0.52 MPa and 1.2 MPa overpressure trials. The three plots correspond to the highest tissue temperature achieved in each of the three experimental groups. For the control trial, where no overpressure was used, the boiling point of water is 100 ºC. The highest tissue temperature measured among the 6 control runs was 92.6 ºC.
The highest tissue temperature attained among the 0.52 MPa and 1.2 MPa runs were 143.2 ºC and 133.3 ºC respectively.
60 150
100
50 Tissue temperature Tissue (celsius)
Control 0.52 MPa 1.2 MPa 0 0 2 4 6 8 10 12 Treatment time (minutes)
Figure 2.15: Maximum tissue temperatures attained in control, 0.52 MPa and 1.2 MPa condition for 4.8 MHz ablation experiments.
Figure 2.16 shows representative TTC stained sections of tissue from control, 0.52 MPa and
1.2 MPa overpressure trials. Figure 2.17 is a summary of ablated areas, treated areas, ablated depths and treated depths for the three experimental groups. The bar graphs show that treated area increased from 2.65±0.40 cm2 for the six control trials to 3.31±0.47 cm2 for the six 0.52
MPa trials and 3.52±0.75 cm2 for the six 1.2 MPa trials. Ablated area, ablated depth and treated depth showed no evident trend with increasing overpressure.
Results from Student t-tests comparing emission levels and lesion geometry for the three groups are shown in Table 2.5. Statistically significant (p≤0.05) results have been highlighted. It can be seen that the 1.2 MPa overpressure trials had significantly lower subharmonic (t =
−3.1859, p = 0.0097) and low-frequency (t = −2.8891, p = 0.0161) emission levels in comparison to control. The 1.2 MPa trials also had a significant increase in treated area (t = 2.5558, p =
61 0.0286) in comparison to control. The 0.52 MPa overpressure trials had a significant increase in treated area (t = 2.6111, p = 0.026) in comparison to control trials. While comparing results from
1.2 MPa trials to those from 0.52 MPa trials, no statistically significant differences were seen.
Control 0.52 MPa
1.2 MPa
Figure 2.16: Representative TTC stained sections from tissue ablated at 4.8 MHz under control, 0.52 MPa and 1.2 MPa conditions showing central ablated region in tan, ring of partially viable tissue in pink and untreated viable tissue in red.
62
4.5 a 1.2 MPa 4 0.52 MPa Control 3.5
3 ) 2 2.5
2 Area (cmArea 1.5
1
0.5
0 Ablated Treated
2.5 1.2 MPa b 0.52 MPa 2 Control
1.5
depth( cm) depth( 1
0.5
0 Ablated Treated
Figure 2.17: Comparison of treatment area (a) and treatment depth (b) among control, 0.52 MPa and 1.2 MPa conditions for the 4.8 MHz ablation experiments.
63
SH BB LF AA TA AD TD 1.2 MPa t = −3.1859 t = −0.8977 t = −2.8891 t = 1.5587 t = 2.5558 t = 2.1073 t = 1.9972 w.r.t p = 0.0097 p = 0.3905 p = 0.0161 p = 0.1501 p = 0.0286 p = 0.0613 p = 0.0737 control
0.52 MPa t = −2.0570 t = −2.0564 t = −1.5734 t = 0.1800 t = 2.6111 t = 1.3713 t = 0.928 w.r.t p = 0.0667 p = 0.0668 p = 0.1467 p = 0.8607 p = 0.026 p = 0.2003 p = 0.3753 control
1.2 MPa t = −1.0433 t = 1.4879 t = −0.7272 t = 1.5999 t = 0.6331 t = 1.1972 t = 1.2213 w.r.t p = 0.3214 p = 0.1676 p = 0.4838 p = 0.1407 p = 0.5409 p = 0.2588 p = 0.2500 0.52 MPa
Table 2.5: t statistics (t) and probability (p) values for comparing averaged acoustic emission levels and treatment geometry across control, 0.52 MPa and 1.2 MPa conditions in 4.8 MHz experiments. Significant p values (p <0.05) have been highlighted.
The three acoustic emission levels (SH, BB and LF) were correlated to the four lesion
geometry metrics (AA, TA, AD, TD) and the results are shown in Table 2.6. Correlations
between acoustic emissions and lesion geometries are highlighted if statistically significant
(p≤0.05). Subharmonic emission levels correlated negatively to ablated area (r = −0.5448, p =
0.0194), treated area (r = −0.6022, p = 0.0082), ablated depth (r = −0.7025, p = 0.0012) and
treated depth (r = −0.7268, p = 0.0006). Broadband emission levels correlated negatively with
treated area (r = −0.6822, p = 0.0018), ablated depth (r = −0.586, p = 0.01069) and treated depth
(r = −0.599, p = 0.0086). Low-frequency emission levels correlated negatively with ablated area
(r = −0.478, p = 0.0448), treated area (r = −0.5894, p = 0.01), ablated depth (r = −0.7182 p =
0.0008) and treated depth (r = −0.6686, p = 0.0024).
64 SH BB LF AA TA AD TD
SH 1
r = 0.238 BB p = 0.3415 1
r = 0.3965 r = 0.5758 LF p = 0.1033 p = 0.0124 1
r = −0.5448 r = −0.0094 r = −0.478 AA p = 0.0194 p = 0.9706 p = 0.0448 1
r = −0.6022 r = −0.6822 r = −0.5894 r = 0.5134 TA p = 0.0082 p = 0.0018 p = 0.01 p = 0.0293 1
r = −0.7025 r = −0.586 r = −0.7182 r = 0.6003 r = 0.7662 AD p = 0.0012 p = 0.0106 p = 0.0008 p = 0.0084 p = 0.0002 1
r = −0.7268 r = −0.599 r = −0.6686 r = 0.6162 r = 0.7854 r = 0.9285 TD p = 0.0006 p = 0.0086 p = 0.0024 p = 0.0065 p = 0.0001 p = 0 1
Table 2.6: Correlation coefficients (r) and corresponding probability values (p) for correlating acoustic emission (SH, BB and LF) levels to treatment geometry (AA, TA, AD and TD) for the 4.8 MHZ experiments. Significant correlations are highlighted.
2 Intercept SH BB (B2) LF (B3) 1.2 MPa 0.52 MPa P R (B0) (B1) (B4) (B5) AA 0.4037 −0.557 0.3153 −0.5866 −0.4979 −0.7132 0.0659 53.97 % (p = 0.37) (p = 0.0521) (p = 0.2687) (p = 0.0616) (p = 0.4754) (p = 0.2651) TA −0.1435 −0.3405 −0.6552 0.0191 0.5574 −0.1270 0.002919 74.29 % (p = 0.6657) (p = 0.1038) (p = 0.0073) (p = 0.9299) (p = 0.2914) (p = 0.7854) AD 0.3118 −0.5871 −0.3492 −0.3916 −0.3213 −0.6141 0.00059 80.58 % (p = 0.2897) (p = 0.0044) (p = 0.0714) (p = 0.0556) (p = 0.4781) (p = 0.1472) TD 0.5407 −0.7105 −0.4719 −0.3037 −0.5805 −1.0416 3.531*10-5 88.11 % (p = 0.0303) (p = 0.00016) (p = 0.0051) (p = 0.057) (p = 0.1168) (p = 0.0056)
Table 2.7: Multivariate multiple regression coefficients (B), probability (p) and total variance explained (R2) for predicting AA, TA, AD and TD in 4.8 MHz experiments, based on equation 2.4. Significant p values have been highlighted.
65 2 SH BB (B2) LF (B3) P R (B1) AA −0.4262 0.4039 −0.5416 0.02171 48.73 % (p = 0.0602) (p = 0.1064) (p = 0.0462) TA −0.4313 −0.5065 0.1268 9.15 * 10-4 68 % (p = 0.0202) (p = 0.0160) (p = 0.5275) AD −0.4927 −0.2508 −0.784 1.11*10-4 76.49 % (p = 0.0036) (p = 0.136) (p = 0.0405) TD −0.544 −0.3123 −0.273 1.11*10-4 76.52 % (p = 0.00174) (p = 0.0688) (p = 0.1256)
Table 2.8: Multivariate multiple regression coefficients (B), probability (p) and total variance explained (R2) for predicting AA, TA, AD and TD in 4.8 MHz experiments, based on equation 2.5. Significant p values have been highlighted.
2 Regression coefficients (B1, B2, B3, B4 and B5) and R values from multivariate multiple regression analysis for the 4.8 MHz experiments, based on model given in equation 2.4 are shown in Table 2.7. It can be seen that overpressure conditions do not regress significantly to most of the outputs except for B5 (Var0.52) which regresses significantly to treated depth. Table
2 2.8 shows the regression coefficients (B1, B2, and B3) and R values from multivariate multiple regression analyses for the 4.8 MHz experiments, based on the model given in equation 2.5, after dropping categorical variables for the overpressure conditions. It can be seen that models predicting ablated area, treated area, ablated depth and treated depth were statistically significant, indicating that at least one of the inputs had significant regression to the output. The R2 values indicate that the models predicted between 48% to 76% of the output variance.
Table 2.8 shows that broadband emission levels negatively regressed negatively to treated area only, whereas subharmonic emission levels regressed negatively to treated area, ablated depth and treated depth. Low-frequency emission levels regressed negatively to ablated area and ablated depth.
Comparing p-values for the three acoustic emissions while predicting each of the outputs, it is seen that low-frequency levels followed by subharmonic emission levels were the most
66 significant in predicting ablated area. For predicting treated area, broadband levels followed by subharmonic emission levels were the most significant. In predicting ablated depth, subharmonic emission levels followed by low-frequency levels were the most significant inputs. In predicting treated depth, subharmonic emission levels followed by broadband levels were the most significant inputs.
Comparing p-values for a single acoustic emission input in predicting the four outputs, it can be seen that subharmonic emission levels were the most significant in predicting treated depth followed by ablated depth. Broadband emission levels were the most significant in predicting treated area. Low-frequency emission levels were the most significant in predicting ablated area and ablated depth. It is also important to note that the three acoustic emission levels regressed negatively to the outputs, indicating that an increase in acoustic emissions related to a decrease in thermal lesion geometry for the 4.8 MHz trials.
2.6 Discussion
The results reported in the current study can explain possible mechanisms by which bubble activity and tissue vaporization can affect bulk ultrasound ablation results. The results from 3.1
MHz and 4.8 MHz experiments are discussed under separate sections.
2.6.1 Experiments at 3.1 MHz
Applying 1.2 MPa of static overpressure reduced subharmonic emissions at 3.1 MHz, hence confirming that an overpressure of 1.2 MPa suppressed cavitation activity associated with subharmonic emissions for ablation experiments at 31 W/cm2 of ultrasound intensity.
Another effect of applying overpressure was seen as an increase in broadband emissions earlier (Karunakaran et al. 2008). Previously (Karunakaran et al. 2008) broadband emissions were defined as averaged energy in the frequency band covering 0.4-0.75 MHz. Here, broadband
67 energy is defined as energy in the frequency band 1.2-1.5 MHz to keep acoustic emission definitions comparable across the 3.1 MHz and 4.8 MHz experimental groups. Comparing broadband emission levels across the control and overpressure groups indicated a statistically significant increase in broadband energy from 0.4827±0.1646 dB for the eleven control runs to
0.7255±0.1499 dB for the eleven overpressure runs (t = 4.2090, p = 4.3154*10-4), when defined as energy in the band 0.4-0.75 MHz (Figure 2.18), whereas no significant difference was observed when broadband levels were defined as energy in the band 1.2-1.5 MHz (Table 2.5).
1.5 1.2 MPa 1.3 Control
1.1
0.9
0.7
0.5 Mean emissionlevel Mean (dB) 0.3
0.1
Broadband
Figure 2.18: Comparison of broadband emissions levels, when calculated as energy in the band 0.4-0.75 MHz, across control and 1.2 MPa experiments performed at 3.1 MHz.
It is hypothesized that the reason for such an increase in broadband energy levels in the 0.4-
0.75 MHz band for the tissue experiments (Karunakaran et al. 2008) could be the effect of
68 overpressure on bubble nuclei. Applying overpressure could have caused nuclei to collapse resulting in daughter nuclei, hence causing temporal fluctuations in the number of bubbles with ambient radius. Ambient radius is defined as the radius of the bubble when ultrasound was absent. These kinds of temporal fluctuations in number of bubbles with ambient bubble radii have been shown to produce broadband noise (Yasui et al. 2010). The sensitivity of PCD at 0.4-
0.75 MHz frequency band is greater than at 1.2-1.5 MHz due to the presence of the high pass filter with cut-off at 1 MHz. This could be the reason why the increase in broadband emissions was statistically significant in the 0.4-0.75 MHz band but not in the 1.2-1.5 MHz band.
Based on results of Student t-tests for comparing ablation areas between the control and overpressure experiments (Table 2.1), it was noted that 1.2 MPa experiments had reduced ablated area. It can be said that microbubbles generated from cavitation may redistribute ultrasound energy and hence increase ultrasound energy deposition locally leading to increased tissue attenuation and larger ablated areas (Melodelima et al. 2004b). While applying overpressure, bubble activity is suppressed and hence the energy is not redistributed, leading to smaller ablated areas.
It is seen from Table 2.4 that the model was statistically significant in predicting only the lesion areas and not the lesion depths. From the TTC stained sections, it was noted that the treatment depth almost covered the entire tissue thickness, indicating that the treatment depth was probably constrained by the tissue thickness. It was also evident that samples in both control and overpressure groups were almost always ablated through the whole thickness of the tissue.
This could explain why there were no statistically significant differences in ablated and treated depths evident among the two groups (Table 2.1) and why the models could not predict lesion depths reliably (Table 2.4).
69 Based on Table 2.2, it can be seen that acoustic emission levels regressed positively to ablation geometry metrics for the 3.1 MHz experiments, suggesting that any increase in emission may relate to an increase in lesion area and depth.
It is seen from Table 2.4 that none of the acoustic emission levels regressed significantly to the lesion geometry metrics. A possible reason could be low signal to noise ratio in the 3.1 MHz experiments. In these experiments, the preamplifier employed a signal amplitude gain of 200 to avoid signal clipping. It can be seen from individual emission traces (Figure 2.8) that emission levels were at maximum 5 dB above the noise floor. Based on the statistically significant correlation coefficients between the acoustic emissions and lesion geometry, it is believed that more significant models can be obtained for signals recorded with higher signal to noise ratio.
2.6.2 Experiments at 4.8 MHz
Comparing emissions from the three groups, it was seen that applying 1.2 MPa overpressure reduced subharmonic and low frequency emissions for the 4.8 MHz experiments. Applying 0.52
MPa overpressure delayed the onset of low-frequency emissions in comparison to control experiments. Subharmonic emissions between the control and 0.52 MPa group were not significantly different. These tests confirm that 1.2 MPa overpressure was enough to suppress cavitation for ablation experiments done at 31 W/cm2 (0.92 MPa) of ultrasound intensity. The intermediate level of overpressure (0.52 MPa) increased the tissue boiling point and hence delayed low-frequency emissions but did not affect cavitation activity.
Comparing the acoustic emission levels and lesion geometry (Table 2.5) between the three experimental groups suggests that suppressing cavitation and increasing the tissue boiling point to 189 ºC may increase lesion dimensions by delaying shielding effects and hence increasing ultrasound penetration.
70 Figure 2.13 shows temperature profiles from control, 0.52 MPa and 1.2 MPa runs. It can be seen that applying overpressure increases the tissue boiling point and hence delays the onset of low-frequency emission signals. Tissue vaporization can produce vapor nuclei which in turn can cause cavitation locally (Rabkin et al. 2006). For control runs, where tissue vaporization occurs early in the experiment, vapor nuclei may be generated earlier in the experiment in comparison to the overpressure trials. This may in turn produce higher levels of subharmonic and broadband emissions in comparison to the overpressure trials.
Results from multiple regression analysis (Table 2.8), indicate that acoustic emissions can be used to predict ablation outcomes in the 4.8 MHz bulk ultrasound ablation experiments with high reliability and hence plan and monitor treatments.
Use of thermocouples during HIFU procedures have been shown to distort ultrasound beams
(Hynynen 1989) and to cause viscous tissue heating (Morris 2008). It was recommended that thermocouple probes of diameter greater than or equal to half the square-root of the wavelength
(in mm) can distort the ultrasound beam (Hynynen 1989). Thermocouples of diameter less than or equal to one-fifth of the square-root of the wavelength can produce a local detectable effect, but do not affect the overall temperature distribution significantly (Hynynen 1989). For the 4.8
MHz experiments, the thermocouples of diameter 0.2 mm are about 1/3rd of the square-root of the wavelength (0.57 mm1/2), and hence the thermocouple can cause marginal distortion on the field according to Hynynen (1989). Notably, measurements reported by Hynynen (1989) were for highly focused ultrasound beams, for which a thermocouple probe could distort the field and cause up to 20% reduction in local ultrasound intensities. Morris et al. (2008) measured temperature rises of about 5.4 °C and 2.4 °C at HIFU intensities of 154 W/cm2 and 63 W/cm2 respectively, caused by viscous heating artifact for ex vivo bovine liver ablation at 1.7 MHz.
71 Temperature rises seen in Figure 9 in Morris et al. (2008), were extrapolated for the frequency and ultrasound intensity used in the 4.8 MHz experiments. It is estimated that for HIFU ablation at 4.8 MHz and 31 W/cm2 ultrasound intensity, thermocouples placed in the field may read 3 °C higher than actual temperatures, due to viscous heating artifacts. The authors also found that the contribution of viscous heating artifacts reduces with increasing beam width. For the present bulk ultrasound ablation studies, with beam widths on the order of 2 cm, the expected contribution of thermocouple-related artifacts to local temperature elevation is considerably less than 3 °C.
2.6.3 Comparing 3.1 MHz vs. 4.8 MHz experiments
Comparing overall results for the 2 frequencies indicated that emission levels regressed to ablation outcomes positively for the 3.1 MHz experiments and negatively for the 4.8 MHz experiments. The contradicting effect could be due to the different operating frequencies. Tissue absorption at 3.1 MHz is lower than at 4.8 MHz. This could partially explain why tissue vaporization occurred more frequently in the 4.8 MHz experiments, even though the ultrasound intensities were similar for both frequencies. Absence of low-frequency emission signals may suggest the absence of vapor bubble clouds and therefore the absence of acoustic shielding or shadowing in the 3.1 MHz experiments. For the 4.8 MHz experiments, the higher tissue absorption enabled tissue to reach boiling in about half the time and hence vaporization occurred in many cases as evidenced by the low-frequency signals. The larger bubbles and gas pockets formed from tissue vaporization may shield ultrasound energy, reducing lesion area and depth. It should be noted that the weaker regression coefficients observed at 3.1 MHz were possibly due to low signal to noise ratio, but a higher signal to noise ratio is not expected to change the regression coefficients from positive to negative. The positive regression between acoustic
72 emissions and ablation results at 3.1 MHz may simply be due to the absence of vaporization and shielding at the lower frequency.
The effect of cavitation on ablation depth was not seen in 3.1 MHz experiments. A possible explanation is the different depths of penetration at different frequencies. At 3.1 MHz, depth of penetration is higher than at 4.8 MHz. It could be seen from the TTC stained sections of 3.1
MHz experiments that the treatment depth was constrained by the tissue thickness (Karunakaran et al. 2008). Hence a change in treatment depth could not be observed. In the 4.8 MHz experiments, the depth of penetration was smaller and hence any change in depth could be seen as a statistical difference between the cases.
2.7 Conclusion
The results reported here suggest that bubble activity significantly influences ultrasound bulk ablation. Application of overpressure, suppressed subharmonic emissions associated with stable cavitation at 3.1 and 4.8 MHz. Notably, overpressure significantly decreased the area of tissue ablation in 3.1 MHz experiments, suggesting that suppression of microbubble activity may decrease heat deposition in liver tissue during bulk ultrasound ablation. At 4.8 MHz, overpressure increased thermal lesion dimensions in comparison to control experiments, possibly due to absence of ultrasound energy shielding in overpressure experiments. Further studies with a controlled bubble population may shed light on the exact mechanisms responsible for the differences in the role of cavitation at the two frequencies.
The present study suggests that bubble activity, including cavitation and tissue vaporization, may have contradicting roles in bulk ultrasound ablation. For the lower ultrasound intensities employed in ultrasound bulk ablation without tissue vaporization (3.1 MHz experiments), the primary effect of cavitation may be an effective increase in tissue absorption due to scattering
73 and microbubble oscillations. In the presence of tissue vaporization (4.8 MHz experiments), the vapor cloud produced by tissue boiling may shield energy from the intended treatment zone and cause smaller lesions.
74 Chapter 3
Roles of Thermal and Non-thermal Mechanisms in Tissue Bioeffects for Ex Vivo Bulk Ultrasound Ablation
3.1 Objective
It is important to know about the contribution of thermal (caused by tissue temperature elevation) and non-thermal (caused by cavitation and other non-thermal mechanisms) bioeffects in bulk ultrasound ablation. Knowledge of relative contributions of thermal and non-thermal mechanisms in lesion formation may facilitate treatment planning by controlling non-thermal mechanisms if needed. It was hypothesized that triphenyl tetrazolium chloride (TTC) staining can be used as an indicator of thermal dose and hence as a way to measure thermal bioeffects.
Hence, TTC uptake for tissue heated in a water bath was associated to its thermal dose.
It was also hypothesized that for bulk ultrasound ablation, bioeffects due to non-thermal mechanisms including cavitation were negligible. To test this hypothesis, bioeffects between tissue samples heated to a similar thermal dose with cavitation (ultrasound treated) and without cavitation (water bath heated) were compared for several thermal dose levels. Experiments presented in this chapter were designed to test two main hypotheses.
1. TTC uptake can be used as an indicator of thermal dose attained locally in tissue treated
by bulk ultrasound and by conductive heating.
2. Cavitation during bulk ultrasound ablation alters tissue bioeffects as measured by TTC
uptake.
75 TTC can be used to assess viability of thermally treated tissue as shown in Chapter 2. The three levels of TTC uptake, i.e. complete, partial and no TTC uptake shown in Figure 2.5, represent different tissue viability regions. To evaluate the association of TTC uptake to thermal dose, bovine liver samples were heated in a water bath to a range of tissue temperatures (25 °C to
70 °C). Thermocouples placed in the tissue samples recorded tissue temperature which was later used to estimate thermal dose. The liver samples were stained with TTC. TTC uptake was evaluated as a function of thermal dose of the heated tissue at the location of thermocouple.
Bioeffects of ultrasound caused by thermal and non-thermal mechanisms can be estimated by evaluating tissue viability through staining methods like TTC. Thermal bioeffects caused by hyperthermia during ultrasound ablation can be estimated by measuring thermal dose as described in equation 1.1. Bioeffects caused by non-thermal mechanisms (including cavitation) during ultrasound bulk ablation can be evaluated by comparing tissue viability, as characterized by TTC uptake, between ultrasound treated and thermally heated tissue, with comparable thermal doses.
To compare tissue viability between ultrasound treated and thermally heated tissue, thermal doses from ultrasound treated tissue samples reported in Chapter 2 were replicated in a water bath heating setup. Liver samples were heated in a water bath to replicate thermal doses from the ultrasound experiments. A thermocouple was inserted in the tissue to record tissue temperature throughout the experiment and hence to evaluate thermal dose to compare to the ultrasound experiments. TTC uptake at the position of the thermocouples for the ultrasound and water bath heated tissue samples was recorded. TTC uptake was graded qualitatively between 0 and 3 with
0 representing viable tissue, 1 representing partially viable tissue, 2 representing nonviable tissue and 3 representing carbonized tissue regions.
76 Logistic regression models were built to predict the probability that the tissue was coagulated
(TTC score >1) for a given thermal dose value and experimental condition (ultrasound treatment or water bath heating). Testing the hypothesis that the regression coefficient corresponding to the experimental condition (i.e. ultrasound or water bath) is equal to zero, confirms or denies the role of cavitation in altering tissue bioeffects and tissue viability as characterized by TTC uptake.
3.2 Materials and methods
A series of tasks namely calibrating water bath setup, evaluating TTC uptake as a function of thermal dose, matching ultrasound thermal dose curves in water bath setup and building logistic regression model to predict tissue coagulation based on thermal dose and experimental condition were required for testing the two hypotheses in this chapter. The methods required for each of these tasks are described below.
3.2.1 TTC vs. thermal dose
Figure 3.1 shows the experimental setup for evaluating TTC uptake as a function of thermal dose.
TTC staining has until recently been used to delineate ablation boundaries (Kinsey 2008,
Bronskill 2007). It is also known that local TTC uptake level has some correlation to local tissue temperatures, thermal dose and tissue viability (Bronskill 2007). The experiment described in this section was designed to investigate the association between TTC uptake and thermal dose in thermally heated tissue, and the possibility of employing TTC staining as a method to evaluate thermal bioeffects in bulk tissue samples. Figure 3.1 shows the experimental setup with the digital water bath circulator (Isotemp 2150, heated bath circulator, Fisher Scientific). Twelve pieces of liver tissue were cut to fit tissue containers, 57∙57∙7 mm3 in volume each. The containers were sealed with Tegaderm (3M Tegaderm transparent dressing, Nexcare) and saline was injected into the sealed containers to fill empty spaces. A needle syringe was used to inject
77 saline and remove air bubbles. Duct tape was used to enforce an airtight seal around each container. Ten of the twelve samples were arranged inside the bath equidistant from the heater.
The eleventh tissue sample, with a needle thermocouple inserted in it, was placed inside the water bath with a 1 MHz unfocused PCD (C302, Panametrics, Waltham, MA, USA) positioned above the tissue (Figure 3.1).
The twelfth tissue sample was placed on the benchtop near the water bath setup in a separate fluid filled container with another unfocused 1 MHz PCD (V302, Panametrics, Waltham, MA,
USA) positioned on top of the tissue. The two PCDs record acoustic signals from the two tissue samples, one sample which was heated up to 70 °C positioned in the water bath and the other which was at room temperature positioned on the benchtop outside the water bath setup. PCD
V302 was included to record any acoustic noise associated with the setup which could later be compared to the signal from PCD C302 to estimate acoustic emission signals from tissue heated in the water bath.
PCD signals were band-pass filtered (0.3 kHz-1 MHz), amplified (SR 560, Stanford
Research Systems, Elgin, IL), and recorded at a 10 MHz sampling rate using a digital oscilloscope (Waverunner, Lecroy, Chestnut Ridge, NY). Signals of 1 s duration were recorded once every second for 20 seconds at 5 minute intervals throughout the experiment. The water bath heater was turned off while the PCD signals were acquired to avoid low-frequency noise caused by movement in the bath circulator. The recorded signals were later processed using the frequency analysis algorithm described in section 2.3 to quantify acoustic emission levels associated with tissue heated up to 70 °C in the water bath.
Another type K thermocouple was inserted in one of the ten samples placed close to the heater. The water heater digital dial was set to 80 °C. One tissue sample was taken out of the
78 water bath at 25 °C, sliced halfway through the tissue thickness and stained with 2% TTC
solution for 45 minutes. The procedure was repeated for the other tissue samples, removing one
sample at every 5 °C interval, except for one sample which was removed at 43 °C and stained.
The tissue sample placed under the PCD C302 setup was removed last out of the water bath,
when the tissue temperature had reached 70 °C. A thermologger (Omegaette HH306, Omega
Engineering, Stamford, CT, USA) recorded the tissue temperature at 2 second intervals
throughout the experiment.
Digital dial
PCD with water cooling Tissue samples
Thermocouple
Figure 3.1: Water bath setup showing tissue samples placed in the water bath and a PCD positioned to record acoustic signals from the heated tissue.
3.2.2 Calibration of analog water heater
An analog model of the water bath heater (Model 71, Fisher Scientific) was used for the
experiments described in section 3.2.3. The heating characteristics of the analog water bath
heater were unknown and hence calibration of the water bath was required. The experimental
setup for water bath calibration is shown in Figure 3.2. Figure 3.2 shows the water bath
circulator (Model 71, Fisher Scientific) immersed in a water bath (31∙19.5∙12 cm3), containing
79 1.8 liters of deionized water. A piece of liver tissue was cut to fit the plastic tissue container, filled with saline and sealed with Tegaderm (3M, St. Paul, MN) and duct tape as described in section 3.2.1. Two 0.4 mm needle thermocouples (Type B, Ella CS, Hradec Králové, Czech
Republic) were used to record the water bath and tissue temperatures respectively. One thermocouple was inserted into the tissue center to record tissue temperature and the other thermocouple was placed in the water bath to record water temperature. The tissue was placed in the water bath and the water bath was covered with a steel lid to reduce heat loss.
Experiments were done at various heater dial settings, ranging from 30 °C to 65 °C. A new tissue sample was used for each dial setting. The dial was set at a desired value and the water bath was allowed to heat the tissue sample for 20 minutes. Throughout the experiment, the two thermocouple readings were recorded every 2 minutes starting from the moment the heater was turned on. The results from this series of experiments were used to evaluate heating characteristics of the water bath setup, as used in the later experiments.
Heater dial
Heater coil Water bath
Steel lid
Thermologger
Figure 3.2: Experimental setup for water bath calibration showing the water heater and the covered water bath.
80 Figure 3.3 shows the calibration curve for the heater. The figure shows final tissue temperatures reached in 20 minutes for dial settings ranging from 30 °C to 65 °C. Based on the curve, it is seen that the dial setting underestimates water bath temperature by at least 10 °C. It is also seen that for higher temperatures, the water bath takes longer to attain the final steady-state temperature. The data from Figure 3.3 was used to calibrate the heater dial. This information was used in planning dial settings for the water bath heating experiments described in the next section.
Water bath calibration
100
90
80
70 30 Celsius 60 35 Celsius 40 Celsius 45 Celsius 50 50 Celsius 55 Celsius 40 60 celsius
65 celsius Water temperature (celsius) temperature Water 30
20
10
0 0 2 4 6 8 10 12 14 16 18 20 Time (minutes)
Figure 3.3: Calibration curve for analog water bath heater at various dial settings.
Figure 3.4 shows the tissue heating curves shown in Figure 3.3, but with data points limited to the linear heating observed at the start of the experiments. A linear curve was fit to the 65 °C heating curve and the equation is shown in the figure. The equation has an intercept of 27.99 °C,
81 which was the average initial tissue temperature. The slope of the curve provides us with the initial heating rate of the water bath, about 5 °C/minute.
Figure 3.5 shows the final water bath and tissue temperatures for each of the water bath heater dial settings. It can be seen that the tissue temperature closely follows the water bath temperature indicating good heat conduction from the water bath to the tissue samples sealed inside containers.
Water bath heating rate
120
y = 5.1101x + 27.992
100
80 30 Celsius 35 Celsius 40 Celsius 45 Celsius 60 50 Celsius 55 Celsius 60 celsius 65 Celsius
40 Linear (65 Celsius) Waterbath temperature (Celsius)
20
0 0 2 4 6 8 10 12 14 16 Time (minutes)
Figure 3.4: Linear portion of heating curves at various heater dial settings and linear regression line showing the rate of water bath heating.
3.2.3 Replicating ultrasound thermal dose
The goal of these experiments was to replicate thermal doses attained in tissue samples treated by 4.8-MHz ultrasound, described in chapter 2, section 2.2.5. Ultrasound experiments with
82 maximum tissue temperature less than 90 °C were chosen. The thermocouple data was used to calculate cumulative thermal dose throughout the experiment based on equation 1.1. The log10 of the cumulative thermal dose termed as ‘log10 cumulative thermal dose’ was calculated for selected ultrasound experiments.
Figure 3.6 shows tissue temperature and log10 cumulative thermal dose curves for four selected ultrasound trials. The maximum tissue temperature in the four trials were 46.4 °C,
86.6 °C, 72.4 °C and 52.5 °C respectively. The corresponding log10 cumulative thermal doses for the four trials were 0.6812, 12.48, 8.65 and 2.636.
120
100
80
60 Actual Temperature (°C) Temperature Actual 40
20
0 0 10 20 30 40 50 60 70 80 90 Dial Setting (°C) Water Bath Liver Tissue Figure 3.5: Calibration curve for water bath calibration showing water and liver tissue temperatures at the end of 20 minutes for each dial setting.
The experimental setup is similar to that described in section 3.2.1. Tissue samples were cut to fit the sample container, sealed with Tegaderm, filled with saline and placed in the water bath.
83 The water-heater dial was set to a desired set point, calibrated from data in Figure 3.3 to obtain required final tissue temperatures that matched the ultrasound trials.
The calibration curves in Figure 3.3 were used to decide on the dial settings appropriate for replicating the tissue temperature curves given in Figure 3.6. For Trial 1, the heater dial was set to 35 °C in order to obtain a final tissue temperature of about 46.4 °C. For trial 2, where the final tissue was 86.6 °C for the ultrasound experiment, the heater dial was set at 60 °C. For trial 3, the water-heater dial was set at 51 °C, in order to attain a final tissue temperature of 72.4 °C at the end of 20 minutes. For trial 4, the water heater dial was set at 40 °C to obtain a final tissue temperature of 52.5 °C at the end of 20 minutes of heating.
3.2.4 TTC uptake scores
Heated tissue slices were stained with 2% TTC to evaluate thermal bioeffects based on TTC uptake and tissue appearance. Figure 3.7 shows a piece of ultrasound treated tissue that has been stained with TTC and illustrates the grading system for characterizing TTC uptake. Viable tissue which takes up TTC appears dark red and is given a score of 0. The region of tissue appearing pink which is partially viable and takes up TTC partially is given a score of 1. The tan region which is considered nonviable and does not take up TTC is given a score of 2. The grey/black region, where tissue carbonization has occurred because of tissue vaporization, is given a score of 3.
The four levels of TTC scores were divided into two classifications, uncoagulated or coagulated. TTC scores of 0 or 1, corresponding to complete or partial TTC uptake, were classified as uncoagulated. Regions of tissue with TTC scores of 2 and 3, corresponding to no
TTC uptake and tissue darkening respectively, were classified as coagulated. The new
84 classification coagulated or uncoagulated was represented as 0 for uncoagulated and 1 for coagulated.
3.2.5 Proportional odds model
TTC uptake scores between the ultrasound and water bath experiments were compared using a proportional odds model, an ordered logistic regression model allowing for more than two ordered responses.
The model used here has two inputs, the log10 of thermal dose (TD), which is a continuous variable, and experimental condition (expt), which is a binary variable equal to 0 for water bath experiments and 1 for ultrasound experiments. The ordered response variable Y takes values 0
(uncoagulated) or 1 (coagulated). The model predicts the probability that the tissue will be coagulated (1) for given TD and expt values. Equation 3.1 gives the equation for predicting the output.
Probability of tissue coagulation, P{Y=1} = e(α + β (TD) + γ (expt)) , (3.1) (α + β (TD) + γ (expt)) 1+ e where α, β and γ are the regression coefficients corresponding to the intercept, log10 thermal dose
(TD) and experimental condition (expt) respectively. Since there are only two levels of response variable (Y), 0 or 1, Y = 0 is taken as baseline. Also, Probability (Y=0) + Probability (Y=1) = 1.
Hence the probability that tissue would not be coagulated can be estimated as P{Y=0} = [1-
P{Y=1}].
By testing the hypothesis that β = 0, we can determine whether the log10 thermal dose (TD) has an effect on determining probability of tissue coagulation. Testing the hypothesis γ = 0 will test the significance of the experimental condition, i.e. ultrasound treatment or water bath heating, in determining tissue viability for a given thermal dose value. The result of this hypothesis
85 testing (H0, γ = 0) will evaluate the effect of cavitation on tissue bioeffects and tissue viability as seen by TTC uptake.
90
80
70
60
50
40
Tissue temperature Tissue (Celsius) 30 Trial 1 Trial 2 20 Trial 3 Trial 4 10 0 2 4 6 8 10 12 Time (minutes)
15
10
5
0
-5
-10 Log10 cumulativeLog10 dosethermal Trial 1 Trial 2 -15 Trial 3 Trial 4 -20 0 2 4 6 8 10 12 Time (minutes)
Figure 3.6: Tissue temperature and log10 cumulative thermal dose traces for four 4.8 MHz ultrasound ablation trials.
86 Thermocouple 2 Thermocouple 1
Score 0 Score 2
Score 1 Score 3
Figure 3.7: Ultrasound treated tissue showing three TTC uptake regions scored from 0 to 3.
3.3 Results
3.3.1 TTC uptake vs. cumulative thermal dose
Figure 3.8 shows the log10 thermal dose values at instances in the experiment when a tissue sample was removed for TTC staining and the corresponding TTC stained tissue sections. It is seen that the tissue slices took up TTC completely until the tissue temperature was 43 °C. Once the tissue was heated above 43 °C, TTC uptake reduced gradually until 70 °C, when the tissue lost viability completely and took up no TTC at all.
Tissue samples heated to 25 °C, 30 °C, 35 °C, 40 °C, 43°C and 45 °C, with thermal doses
-10.8 2.5 between EM43= 10 and 10 minutes, took up TTC stain and appeared red, for a score of 0 based on the TTC grading system shown in Figure 3.7. Tissue samples heated above 45 °C, but
4 7 less than 70 °C, with thermal doses between EM43= 10 and 10 minutes which take up TTC partially and appear pink, were given a score of 1. The last sample which was heated up to 70 °C,
8.968 with a thermal dose of EM43= 10 minutes, was given a score of 2.
Figure 3.9 shows broadband and low-frequency emission levels from two tissue samples, one placed inside the water bath and other placed outside the water bath. The curve marked C302 corresponds to the tissue sample that was placed inside the water bath and heated up to 70 °C.
87 The curve marked V302 corresponds to the tissue sample placed outside the water bath and was not heated. It can be seen that low-frequency and broadband emission levels remained unchanged for most of the experiment except for a sudden jump at around 60 minutes for both the samples. It is also seen that low-frequency and broadband emission curves for the two samples are very similar. It can be concluded from the plot that there were no significant low- frequency or broadband emissions for tissue heated up to 70 °C in comparison to the control tissue which was not heated. The sudden 20 dB jump in broadband energy and 30 dB jump in low-frequency energy were apparently due to stray noise in the experimental setup and not tissue vaporization or cavitation. Figure 3.10 confirms the absence of cavitation in tissue heated by the water bath setup.
Figure 3.8: TTC stained liver tissue slices heated to various temperatures from 25 °C to 70 °C showing slight decrease in TTC uptake after 43 °C and significant decrease in TTC uptake after 50 °C.
88 40
35
30
25 LF-C302 20 BB-C302 15 LF-V302 BB-V302
10 Emission Emission level (dB) 5
0
0 10 20 30 40 50 60 70 80 90 -5 100 110 120 Time (minutes)
Figure 3.9: Low-frequency (LF) and broadband (BB) emission levels from tissue heated in water bath (C302) and control tissue maintained at room temperature (V302).
3.3.2 Matching ultrasound thermal dose
Figures 3.10 through 3.13 show the tissue temperature and log10 thermal dose plots for the four ultrasound trials and their corresponding water bath experiments along with TTC stained sections from the ultrasound and matching water bath experiments. The red curves correspond to the ultrasound experiments which typically ran for 10 minutes. The green curves correspond to water bath experiments, which typically ran for 20 minutes. In the figures, the positions of thermocouples have been highlighted. TTC score was evaluated at the position of the thermocouple only.
For ultrasound trial 1 (Figure 3.10), with maximum tissue temperature of 46.4 °C and log10 cumulative thermal dose of 0.6812, two water bath experiments were performed trying to match
89 the ultrasound tissue temperature and thermal dose curve. One of the water bath experiments, represented by the solid green curve, had a maximum tissue temperature of 44.6 °C and log10 cumulative thermal dose of 0.6611. The other water bath experiment, represented by the dashed green curve, had a maximum tissue temperature close to the ultrasound experiment of 46.4 °C, but the log10 cumulative thermal dose of 0.8311 did not match that of the ultrasound data from
Trial 1. The TTC score for tissue treated by ultrasound was 1. The TTC score for tissue from the matching water bath trial with log10 cumulative thermal dose of 0.8311 (dashed green line) was also 1. The TTC score for the other water bath experiment with thermal dose of 0.6611 was 0.
Figure 3.11 shows the tissue temperatures and log10 thermal dose plots for ultrasound trial 2 and the corresponding water bath experiment. For the second ultrasound trial (Trial 2) with maximum tissue temperature and log10 thermal dose of 86.6 °C and 12.48 respectively, the corresponding water bath experiment produced a maximum tissue temperature and log10 cumulative thermal dose of 81.5 °C and 12.52 respectively. TTC scores for both the ultrasound and water bath experiment were 1. In this instance, the water bath experiment was able to fairly well replicate the ultrasound heating characteristics of Trial 2.
For Trial 3 (Figure 3.12), the ultrasound experiment had maximum tissue temperature and log10 cumulative thermal dose values of 72.4 °C and 8.65 respectively. The corresponding water bath experiment had maximum tissue temperature log10 and cumulative thermal dose values of
72.2 °C and 9.49 respectively. For Trial 3, though the tissue temperature was well matched with the ultrasound experiment, the final thermal dose could not be well replicated. TTC scores for tissue from both the ultrasound and water bath experiments were 2.
90 Trial 1 50 a 40
30
20 Ultrasound Water bath 10 Tissue temperature Tissue(Celsius) temperature 0 5 10 15 20 25
5
0
-5
-10
-15
-20
0 5 10 15 20 25 Log10 cumulativeLog10 dosethermal Time (minutes)
b
Ultrasound
Water bath (dashed) Water bath (solid)
Figure 3.10: Tissue temperature, log10 cumulative thermal dose curve (a) and corresponding TTC stained tissue sections (b) for ultrasound trial 1 (red) and matched water bath experiments (green solid and dashed).
91 Trial 2 100 a Ultrasound 80 Waterbath 60
40
20
0 Tissue temperature Tissue(Celsius) temperature 0 5 10 15 20 25
20
10
0
-10
-20
0 5 10 15 20 25 Log10 cumulativeLog10 dosethermal Time (minutes)
b
Ultrasound Water bath
Figure 3.11: Tissue temperature, log10 cumulative thermal dose curve (a) and TTC stained tissue sections (b) for ultrasound trial 2 (red) and matched water bath experiments (green).
92 a Trial 3 80 Ultrasound 60 Water bath
40
20
0 Tissue temperature Tissue(Celsius) temperature 0 5 10 15 20 25
10
0
-10
-20
0 5 10 15 20 25 Log10 cumulativeLog10 dosethermal Time (minutes)
b
Ultrasound Water bath
Figure 3.12: Tissue temperature, log10 cumulative thermal dose curves (a) and TTC stained tissue section (b) for ultrasound trial 3 (red) and matched water bath experiments (green).
Ultrasound trial 4 (Figure 3.13) had a maximum tissue temperature of 52.5 °C and a log10 cumulative thermal dose of 2.636. The corresponding water bath experiment had a maximum tissue temperature of 48.5 °C and a log10 cumulative thermal dose of 2.530, close to the ultrasound trial. TTC scores for tissue from both ultrasound and water bath experiments were 1.
93 Trial 4 60 a Ultrasound 50 Water bath 40
30
20
10 Tissue temperature Tissue(Celsius) temperature 0 5 10 15 20 25
5
0
-5
-10
-15
-20
0 5 10 15 20 25 Log10 cumulativeLog10 dosethermal Time (minutes)
b
Ultrasound Water bath
Figure 3.13: Tissue temperature, log10 cumulative thermal dose curves (a) and TTC stained tissue sections (b) for ultrasound trial 4 (red) and matched water bath experiment (green).
3.3.3 Proportional odds model
Log10 thermal doses and TTC scores for thermocouple locations from the 4.8-MHz ultrasound experiments and from the water bath experiments described in the earlier sections were pooled together to build the data set for a proportional odds model. The data from the two experiments were pooled together but can be differentiated by the categorical variable expt, which was 0 for water bath experiments and 1 for ultrasound experiments. Figure 3.14 gives a summary of all ultrasound and water bath experimental data used for building the proportional odds model
94 described in equation 3.1. The data pool includes 17 ultrasound data points and 16 water bath data points.
3
2.5
2
1.5 TTC TTC score
1
0.5 Ultrasound Water bath 0 -15 -10 -5 0 5 10 15 20 25 30 Log10 cumulative thermal dose
Figure 3.14: Summary of TTC scores for range of log10 cumulative thermal doses for ultrasound and water bath experiments.
Logistic regression modeling was done using the Design package available in the R statistical programming package (R 2.12.1). R was used to estimate the model parameters, i.e. regression coefficients α, β and γ for the regression model. The Design package assumes the lowest level of the ordered response variable (Y=0) as baseline. From the output of the Design package, the regression coefficients α, β and γ were estimated as −8.1889, 0.8615 and 0.6702 respectively.
95 These values of regression coefficients were substituted in equation 3.1 to obtain probability of tissue coagulation (P{Y=1}) for log10 cumulative thermal dose (TD) values between −5 to 15 and experimental conditions (expt) of 0 and 1. By substituting the regression coefficient values in equation 3.1, the same equation can be rewritten as equation 3.2.
Probability of tissue coagulation, P{Y=1} = e( −8.188 + 0.8615∙(TD) + 0.6702∙ (expt)) (3.2) (−8.188 + 0.8615∙ (TD) + 0.6702∙ (expt)) 1+ e
Based on equation 3.2, P{Y=1} and P{Y=0} were calculated for range of TD and expt values.
Figure 3.15 shows the probability that tissue would be coagulated and probability that tissue would not be coagulated for log10 cumulative thermal dose values ranging from −5 to 15, for ultrasound (expt=1) and water bath (expt=0) experiments. It is seen that the probability of tissue coagulation increases once the tissue reaches a log10 cumulative thermal dose value of 5 and reaches 100% for tissue with a log10 cumulative thermal dose value of about 12.
Figure 3.16 shows the same curves provided in Figure 3.15, but rearranged to better estimate the thermal dose threshold for tissue coagulation for ultrasound and water bath experiments. It is seen that the thermal dose threshold for a 50% chance of tissue coagulation occurs at 108.72 minutes for the ultrasound experiments and at 109.505 minutes for the water bath experiments.
Three hypotheses H0: α = 0, H0: β = 0, H0: γ = 0 were tested to determine the significance of each input variable (intercept, TD and exp) in predicting the probability of tissue coagulation.
The hypothesis tests provided probability (p) values for each of the hypothesis being tested. For cases where p-values were significant (p < 0.05), the corresponding input variable was considered significant in predicting the probability of tissue coagulation. The p-values corresponding to the intercept (p = 0.0209) and thermal dose TD (p = 0.0218) were significant.
The p-value corresponding to experimental condition (exp) was not significant (p = 0.6503).
Based on these p-values, it can be concluded that log10 cumulative thermal dose is significant in
96 predicting probability of tissue coagulation, whereas the experimental condition has no discernible effect on the probability of tissue coagulation in these experiments.
1
0.5 Probability
0 -5 0 5 10 15 Log10 thermal dose Ultrasound Water bath 1
0.5 Probability
0 -5 0 5 10 15 Log10 thermal dose
Figure 3.15: Probability of tissue coagulation (top) and tissue remaining uncoagulated (bottom) for range of log10 cumulative thermal dose for ultrasound and water bath experiments.
97 1
0.5 Probability
0 -5 0 5 10 15 Log10 thermal dose Coagulated Uncoagulated 1
0.5 Probability
0 -5 0 5 10 15 Log10 thermal dose
Figure 3.16: Probability of tissue coagulation for range of log10 cumulative thermal dose values for ultrasound (top) and water bath (bottom) experiments.
3.4 Discussion
3.4.1 TTC as an indicator of thermal dose
TTC is a salt that is reduced to red formazan by viable cells with intact mitochondrial activity.
Nonviable cells with no mitochondrial activity do not reduce the salt and hence do not take up the stain and appear tan. In partially viable tissue regions with trace amounts of mitochondrial activity present, the TTC salt gets reduced but does not produce enough red formazan to stain the tissue red and hence the tissue is stained pink. The decrease in mitochondrial activity may be a sign of thermal bioeffects caused by hyperthermia.
98 Based on the results from water bath experiments designed to evaluate the association of
TTC uptake to thermal dose, it can be concluded that TTC uptake decreased with increasing cumulative thermal dose. The three levels of TTC uptake, i.e. complete, partial and no uptake, corresponded to log10 thermal dose ranges of −10 to 2.5, 4 to 7 and >7 respectively. These results suggest that TTC staining can be used to reliably estimate thermal doses attained at different viability regions in ultrasound treated and thermally heated bulk tissue samples. Hence, TTC staining can be used as an adjuvant technique to existing histological techniques for evaluating thermal lesion boundaries and to estimate thermal dose in a region of tissue. TTC staining can also be used to determine whether thermal dose within a region of interest has crossed a certain limit to ensure complete tissue ablation in the region of interest.
3.4.2 Role of cavitation in altering tissue bioeffects
Figure 3.16 shows a slight, non-significant decrease in the thermal dose threshold needed for
50% chance of tissue coagulation for ultrasound experiments in comparison to the water bath experiments. Thus, it is important to note that ultrasound experiments with cavitation present may need lower thermal doses to produce the bioeffects similar to the water bath experiments.
However, this effect was not statistically significant for the experiments reported here.
Comparing the probability of tissue coagulation between the ultrasound and water bath experiments for a given thermal dose indicated no significant differences between the two cases.
This result suggests that bulk ultrasound ablation exposures, with thermal effects and cavitation, may produce bioeffects similar to heating by pure thermal methods. It can hence be hypothesized that cavitation by itself may cause no noticeable bioeffects during bulk ultrasound ablation but may increase thermal dose attained locally and hence increase thermal bioeffects. Hence, the
99 main role of cavitation in bulk ultrasound ablation may be to alter local thermal dose by redistributing ultrasound energy.
3.5 Conclusion
The results presented in this chapter suggest that TTC staining is a good indicator of thermal dose and hence thermal bioeffects, and that TTC staining can be used as a reliable method to estimate tissue thermal dose to plan ultrasound treatments for soft tissue ablation. The results also suggest that cavitation during bulk ultrasound ablation may not produce any additional bioeffects by itself in comparison to thermal heating of tissue with a matching thermal dose.
Thus, the primary effect of acoustic cavitation in bulk ultrasound ablation may be to produce higher tissue temperatures locally, which in turn may cause enhanced thermal bioeffects as seen by TTC staining.
100 Chapter 4
Relations between Nuclear Histology and TTC Uptake for Liver and Tumor Tissue Treated by Bulk Ultrasound Ablation In Vivo
4.1 Objective
Triphenyl tetrazolium chloride (TTC) is a vital stain used to demarcate lesion boundary for tissue treated by ultrasound and other thermal ablation techniques (Kinsey 2008, Bronskill 2007).
Respiratory mechanisms in viable tissue reduce the tetrazolium chloride into red formazan, which gives the stained tissue a bright red color. Nonviable tissue does not take up the stain and hence appears light in color. Bulk ultrasound ablated tissue (Chapter 2) stained with TTC showed three distinct regions namely, complete TTC uptake (viable), partial TTC uptake
(partially viable) and no TTC uptake (nonviable). The region of no TTC uptake is considered nonviable and the region of complete TTC uptake is considered viable (Kinsey 2008). The region of partial TTC uptake may be considered partially viable, although histology in this intermediate region has not yet been completely understood.
Research addressing histology in the three TTC uptake regions and quantitative comparison of histology between the three regions has not been done previously. Evaluating cellular histology in the partial TTC uptake region and quantification of cellular morphology in the three distinct TTC uptake regions may further validate and strengthen the use of TTC staining to assess cell death in ultrasound thermal ablation.
Boutonnat (1999) has shown that 4', 6-diamidino-2-phenylindole (DAPI), a fluorescent dye, binds to the nucleus of a cell and hence has been used to evaluate necrosis. It has been
101 shown that necrosis can lead to cellular fragmentation that can show up as a decrease in the peak population and an increase in smaller size ranges in the size histogram (Dietzel 1990). Such a decrease in the main population and increase in lower sized population can be revealed by evaluating the changes in number density between treated and control samples for different size ranges (Balaji 2012).
Experimental results described in this chapter were analyzed to quantify and compare cellular histology for each distinct region of TTC uptake (Figure 2.5). Rabbit liver and VX2 rabbit liver tumor were treated in vivo by miniaturized image-ablate arrays using bulk ultrasound ablation
(Mast et al. 2011). The ablated tissue samples were histologically analyzed using TTC vital stain, hematoxylin and eosin (H&E) stain and DAPI nucleic acid stain. Levels of TTC uptake in liver parenchyma, i.e., no TTC uptake (nonviable), partial TTC uptake (partially viable) and complete
TTC uptake (viable) corresponded to three discrete regions of tan, pink and red color respectively. H&E images from the different TTC uptake regions were compared for histological differences. By processing images of DAPI-stained parenchymal tissue from these three TTC uptake regions, cellular damage was quantified. Nuclear size histograms from the three regions were compared to evaluate changes in population number density in each size range. For developing the image-processing algorithm to estimate the nuclear size histogram, DAPI images from known viable, partially viable and nonviable regions of ultrasound treated liver were used.
The algorithm was then validated for few chosen ultrasound treated liver and VX2 tumor samples.
4.2 Materials and Methods
The experiments reported here were conducted as a part of another study (Mast et al. 2011). The original study was aimed at investigating the use of miniaturized image-treat ultrasound probes
102 for in vivo tumor ablation. The tissue samples collected from experimental procedures reported in Mast 2011 were stained and analyzed as described in this chapter. The experimental setup is described in detail in Mast et al. 2011. An overview of the experimental methods is provided below.
4.2.1 Tumor implantation
Solid VX2 tumor masses were induced in rabbit liver lobes by implanting fragments of viable tumor into the left, middle, and right liver lobes (Virmani 2008), followed by 11-14 days of tumor growth. This growth time resulted in solid tumors of ~1 cm diameter suitable for the ablation studies reported here (Mast 2011). Figure 4.1a shows a representative liver lobe with a
1.5 cm VX2 tumor.
4.2.2 Ultrasound ablation
The treatment configuration, shown in Figure 4.1b, consists of an image-treat ultrasound probe similar to those described in chapter 2 (Figure 2.3b). Liver lobes were exposed by laparotomy and mobilized. For treating the tumors, the 3.1 mm diameter, 32-element, 4.8-MHZ ultrasound image-treat array probe was positioned on the liver lobe surface adjacent to the tumor.
A coupling balloon, with chilled water circulating inside for cooling, houses the ultrasound array.
Coupling between the balloon and the liver capsule was achieved by injecting physiologic saline solution and confirmed using B-scan imaging by the image-treat array. The probe position was marked on the liver capsule by an electrocautery device (Mast et al. 2011).
Ablation treatments, performed using the entire 2.320 mm2 array surface firing in unfocused mode, ranged from about 22.5–40.5 W/cm2 spatial-peak, temporal-average intensity for treatment times between 20–120 s. Treatments were performed both on liver lobes containing
VX2 tumor and on normal liver lobes. Table 4.1 shows exposure conditions for all the
103 experiments reported here. The spatial-peak temporal-average intensities (ISPTA) reported in the table are the ratios of acoustic power output from the array, measured using a radiation force balance (Ohmic Instruments Co., Easton, MD), and the active area of the array. The total energy delivered is the product of the intensity, active area and the treatment duration. Some samples of liver and tumor were left untreated to be used as controls. After ultrasound bulk ablation procedures, animals were sacrificed and their livers excised. Treatment regions were bisected along the ultrasound image plane with the electrocautery mark as guidance.
2 Animal Liver lobe Sample type ISPTA (W/cm ) Duration (s) Total energy number delivered (J) 110 RL Liver 34.4 60 950 27 RL Liver 40.5 51 948.6 1 RL Liver 38.5 120 2128 1 LL Liver 38.5 95 1684 27 ML Liver 40.5 102 1897.2 26 RL Liver 22.4 70.5 725 26 RL Tumor 22.4 70.5 725 1 LL Tumor 38.5 95 1684 110 RL Tumor 34.4 60 950 1 RL Tumor 38.5 120 2128 Table 4.1: Ultrasound Intensity, treatment duration and total energy delivered for ultrasound ablation experiments for treating rabbit liver and VX2 tumor tissue.
4.2.3 Histology staining
One face of the treatment region was stained in 2% TTC for 45 minutes. Macroscopic sections stained with TTC were scanned at 300 dpi for image analysis of TTC uptake. Figure 4.2 shows representative sections of nonviable liver, viable tumor and nonviable tumor stained with TTC. It is seen that TTC stain is fully taken up by viable tissue only. The central tan region with no TTC uptake is called nonviable, while the intermediate region of pink with a slight uptake of TTC is called partially viable. It is also seen that viable tumor has some TTC uptake and appears pink, whereas nonviable tumor does not take up the stain and appears pale white.
104 a
b
Figure 4.1: VX2 tumor after 11-14 days of implantation (a). Experimental setup showing ultrasound probe placed on rabbit liver during treatment (b).
The second face of each treatment region was processed for DAPI or H&E staining.
Formalin-fixed tissue was embedded in paraffin, sectioned, and stained with DAPI or H&E.
Tissue processing, H&E staining and DAPI staining protocols are described in Appendix.
105 Hematoxylin in H&E stains the nuclei blue and eosin stains the cytoplasm pink (Figure 4.4 and
Figure 4.6).
Figures 4.3 and 4.5 each show a section of ultrasound treated liver from samples R27 RL liver and R27 ML liver respectively, stained with TTC with clear demarcation between viable and nonviable regions. These figures also show the corresponding H&E slide labeled for regions
1 through 5, corresponding to regions of progressive cell death with region 1 being viable liver and region 5 being nonviable liver. Figures 4.4 and 4.6 show high power (40X) H&E images from the five treatment zones for samples shown in Figures 4.3 and 4.5 respectively.
Complete TTC Partial TTC uptake uptake (viable) (partially-viable)
No TTC uptake (nonviable)
Viable tumor Nonviable tumor
Figure 4.2: Representative sections of nonviable liver parenchyma (R27 ML liver), viable VX2 tumor (R109 RL tumor), and nonviable VX2 tumor (R1 LL tumor) within rabbit liver, each stained with 2% TTC solution.
106 a
b
4
1 2 5
3
Figure 4.3: TTC stained slice and corresponding H&E section of rabbit liver (R27 RL liver) treated at 40.5 W/cm2 for 1 minute. TTC stained slice shows clear demarcation between viable and nonviable tissue (a). H&E section with different zones of ablation marked on it (b).
Normal liver is made up of structural units called lobules. A single lobule has a roughly hexagonal arrangement consisting of hepatocytes arranged in chains also called hepatic cords, radiating outwards from the central vein, evident in zone 1. Sinusoids evident as white spaces
107 receive blood from terminal branches of the hepatic artery and portal vein and deliver it to the central vein. It can be seen from Figures 4.4 and 4.6 that as thermal ablation progresses from normal liver (zone 1) to nonviable liver zones 2-5, the lobule loses its structural integrity with sinusoids becoming prominent, evident inflammation, hemorrhaging and disruption of hepatic cords. Zone 2 shows inflammation and disrupted hepatic cords. Zone 3 shows enlarged sinusoids along with disrupted hepatic cords. Zone 4 shows hemorrhaging along with loss of cell membranes. Zone 5 shows complete loss of hepatic cords along with necroses and scattered hepatocytes.
DAPI is a fluorescent stain taken up by cellular nuclei and makes the nuclei appear bright when excited by ultraviolet light. For DAPI staining, the tissue samples were fixed and processed as described in Appendix. The samples were fixed in 10% neutral buffered formalin solution for
24 hours, embedded in paraffin, then sectioned. The 5 micron thick tissue sections were stained with DAPI by following the DAPI staining protocol described in Appendix. Microscopic DAPI images of the three viability regions were captured using a Nikon IX-81 microscope at 20X
(liver samples) or 40X (tumor samples). Figure 4.7 shows representative DAPI images from the three regions (viable, partially viable and nonviable) of the liver sample. It is seen that nuclei are more numerous and larger in the viable portion of the liver lobe (4.7a) but far less numerous and smaller in the nonviable region (4.7c).
Figure 4.8 shows DAPI images (40X) of viable and nonviable tumor. It can be seen that tumor cells are larger and less regularly shaped than normal hepatocytes. The viable tumor image has numerous tumor cells whereas the nonviable tumor image shows fewer tumor cells, with most of the nuclei being smaller and irregularly shaped due to necrosis (Kerr et al. 1995).
108
Hepatic cord Central vein
2 1
3
4
5
Figure 4.4: Zones 1 through 5 with increasing cell death and necrosis as seen on H&E sections (40X magnification) for sample R27 RL liver.
109 a
b
1
2
3 5 4
Figure 4.5: TTC and H&E stained slices of rabbit liver (R27 ML liver) treated at 40.5 W/cm2 for 2 min showing zones 1 through 5.
110 2
1
1 3 4
5
Figure 4.6: Zones 1 through 5 with increasing cell death and necrosis as seen on H&E sections (40X magnification) for sample R27 ML liver.
111 a
b
c
Figure 4.7: DAPI images (20X) of three regions on an in-vivo ultrasound ablated rabbit liver (sample R1 LL liver). (a) Viable liver (viable) with round healthy nuclei, (b) partially viable region with slightly elongated dying nuclei and (c) nonviable region with dead, scattered nuclei.
112
a
b
Figure 4.8 DAPI images (40X) of viable (a) and nonviable (b) VX2 tumor (sample R26 RL tumor) showing less numerous and smaller nuclei in the nonviable region.
113 4.2.4 Image processing
An image processing algorithm was developed to characterize changes in nuclear size distribution associated with decreasing tissue viability (Dietzel et al. 1990). DAPI images, 1392 pixels wide by 1040 pixels long were captured at 20X magnification (3.1 pixels/μm) for liver samples and 40X magnification (6.2 pixels/μm) for tumor samples. Representative control images were chosen at random and used to develop the image processing algorithm. The captured DAPI images were sharpened using a sharpening filter (Adobe Photoshop). Parameters of sharpening, including intensity and radius of the sharpening mask, were chosen such that there was little or no change in the size of nuclei in the sharpened image in comparison to the original image. Sharpening radii of 5 and 13 pixels for liver and tumor images respectively, and sharpening intensity of 500% for both the liver and tumor images proved suitable in maintaining the nuclei sizes comparable between the sharpened and the original image.
The sharpened images were further processed using MATLAB. Intensity thresholding was performed on the representative liver and tumor DAPI-stained images to identify, count and characterize the cell nuclei. The intensity value used to threshold the images were chosen manually in ImageJ for each image. For each image, the threshold was varied manually in
ImageJ and the threshold value that yielded images best matched to the original image with little or no change in nuclei size was chosen. Thresholded black and white images were processed by a gap-filling algorithm, after which the nuclei were counted. The gap filling algorithm identifies edges of objects and fills objects with solid unbroken edges. This method ensures that only intact nuclei were filled and counted.
Figures 4.9 and 4.10 show representative original and processed (sharpened, thresholded and gap-filled) images from viable, partially viable, and nonviable liver and tumor samples
114 respectively. Area of each counted nuclei within an image was measured, and later used to obtain a size histogram for the image. The nuclear sizes were measured by MATLAB in terms of number of pixels and later converted to µm2 based on the microscope magnification level (20X for liver samples and 40X for tumor samples).
The image processing algorithm was validated by testing the algorithm on randomly chosen partially viable and nonviable liver sample images. Sizes of several randomly chosen nuclei in the image before and after processing were measured manually and compared. This technique of validation ensured that the nuclei size and shape were not altered due to the image processing algorithm.
The validated image processing algorithm was applied to all images and nuclear size histograms were obtained for all the liver and tumor samples. Six images were processed for each TTC uptake region within each tissue sample. Size histograms from the six images were averaged to obtain the mean size histogram. Mean size histograms were calculated for each TTC uptake region for 6 liver samples and 4 tumor samples.
115 a
b
c
Figure 4.9: DAPI images from (a) viable, (b) partially viable, and (c) nonviable liver before (left) and after (right) image processing for sample R1 LL liver.
116 a
b
Figure 4.10: DAPI images from (a) viable and (b) nonviable tumor before (left) and after (right) image processing for sample R26 RL tumor.
4.3 Results
Figure 4.11 shows TTC stained sections of six liver samples from which images of viable, partially viable and nonviable liver were acquired. The samples were named by the animal reference, followed by the lobe and tissue type. The lobes can be identified as RL for right lobe,
117 LL for left lobe and ML for medial lobe. Tissue type was identified as liver or VX2 tumor. For example, R27 ML liver refers to the medial lobe of the liver for animal number 27.
R1 RL liver R110 RL liver Viable liver
Nonviable liver Partially viable liver
R27 RL liver R1 LL liver
R27 ML liver R26 RL liver
Figure 4.11: TTC stained sections for 6 tissue samples showing viable, partially viable and nonviable liver.
Results comparing size histograms between viable, partially viable and nonviable sections from the six liver samples (Figure 4.11) are shown in Figures 4.12, 4.13 and 4.14. The bar graphs indicate the number of nuclei counted in each size range (16-1080 µm2) for every 1 mm2 image area. It was seen that the decrease in the number of nuclei was much more evident in the 209-306
118 µm2 size range than the other size ranges. Table 4.2 shows the comparison of average number of nuclei the 209-306 µm2 size range for the six viable, partially viable and nonviable liver samples.
Figure 4.15 shows the TTC stained sections of nonviable and viable tumor samples. For sample R26 RL, where less than half of the tumor was viable, images were captured from the viable and nonviable regions. The three treated (R1 LL VX2, R110 RL VX2 and R1 RL VX2) and three untreated tumors (R2 ML VX2, R111 RL VX2 and R109 RL VX2) were classified into nonviable and viable tumor groups respectively. Images from the nonviable and viable regions of sample R26 RL were added to the nonviable and viable groups respectively. The four viable and nonviable tumor samples are unpaired.
Results from the image processing algorithm comparing size histograms between viable, and nonviable sections of tumor from the four samples (Figure 4.15) are shown in Figures 4.16 and
4.17. The bar graphs indicate the number of nuclei counted in each size range (8-588 µm2) within an image area of 1 mm2. Size histograms of images from the viable and nonviable regions of the sample R26 RL VX2 are shown in Figure 4.16. The three viable tumor samples, namely
R109 RL VX2, R111 RL VX2 and R2 ML VX2, have been grouped together and their size histograms are shown in Figure 4.17. Figure 4.17 shows the size histograms for the three nonviable tumor sample R1 RL VX2, R1 LL VX2 and R110 RL VX2. Decrease in number of nuclei was more evident in the 105-201 µm2 size range compared to the other size ranges and hence a comparison of average nuclei in the 105-201 µm2 size range for the four viable and nonviable tumor samples are shown in Table 4.3.
119 1600 a 1400
1200
1000
Viable 800 Partially viable
Nonviable Number of Number nuclei 600
400
200
0 16-113 113-209 209-306 306-403 403-500 500-596 596-693 693-790 790-886 886-983 983-1080 Area
4000 b 3500
3000
2500
Viable 2000 Partially viable
Nonviable Number of Number nuclei 1500
1000
500
0 16-113 113-209 209-306 306-403 403-500 500-596 596-693 693-790 790-886 886-983 983-1080 Area
Figure 4.12: Size histograms for viable, partially viable and nonviable regions of liver from samples R110 RL liver (a) and R27 RL liver (b).
120 1600 a
1400
1200
1000
Viable 800 Partially viable
Nonviable Number of Number nuclei 600
400
200
0 16-113 113-209 209-306 306-403 403-500 500-596 596-693 693-790 790-886 886-983 983-1080 Area
2000 b
1800
1600
1400
1200
Viable 1000 Partially viable Non-viable
Number of Number nuclei 800
600
400
200
0 16-113 113-209 209-306 306-403 403-500 500-596 596-693 693-790 790-886 886-983 983-1080 Area
Figure 4.13: Size histograms for viable, partially viable and nonviable regions of liver from samples R1 LL liver (a) and R1 RL liver (b).
121 3000 a
2500
2000
Viable 1500
Nonviable Number of Number nuclei
1000
500
0 16-113 113-209 209-306 306-403 403-500 500-596 596-693 693-790 790-886 886-983 983-1080 Area
b 3000
2500
2000
Viable 1500
Nonviable Number of Number nuclei
1000
500
0 16-113 113-209 209-306 306-403 403-500 500-596 596-693 693-790 790-886 886-983 983-1080 Area
Figure 4.14: Size histograms for viable and nonviable regions of liver from samples R26 RL liver (a) and R27 ML liver (b).
122 Sample Viable Partially viable Nonviable
R1 LL Liver 480 266 204
R1 RL Liver 391 231 177
R110 LL Liver 204 284 195
R27 RL 240 0 186
R26 RL 248 N/A 240
R27 Ml Liver 195 N/A 97
Table 4.2: Comparison of number of nuclei in the 209-306 µm2 size range for the six viable, partially viable and nonviable liver samples. N/A signifies non-availability of partially viable regions in the sample.
123
R26 RL VX2
R1 LL VX2 R2 ML VX2 (control)
R110 RL VX2 R111 RL VX2 (control)
R109 RL VX2 (control) R1 RL VX2
Figure 4.15: TTC stained sections for ultrasound treated (left) and untreated (right) VX2 tumor samples.
124 7000
6000
5000
4000
Viable Nonviable
3000 Number of Number nuclei
2000
1000
0 8-105 105-201 201-298 298-394 394-492 492-588 Area
Figure 4.16: Size histograms for viable and nonviable regions of VX2 tumor from sample R26 RL VX2.
125 14000
12000
10000
8000 R109 RL R111 RL R2 ML
6000 Number of Number nuclei
4000
2000
0 8-105 105-201 201-298 298-394 394-492 492-588 Area
8000
7000
6000
5000
R1 RL 4000 R110 RL
R1 LL Number of Number nuclei 3000
2000
1000
0 8-105 105-201 201-298 298-394 394-492 492-588 Area
Figure 4.17: Size histograms for the three viable (a) and nonviable (b) tumor samples.
126 Viable tumor group Nonviable tumor group
4300 (R109 RL VX2) 1957 (R1 RL VX2)
5017 (R111 RL VX2) 3807 (R1 LL VX2)
4448 (R2 ML VX2) 2740 (R110 RL VX2)
4661 (R26 RL VX2) 4697 (R26 RL VX2)
Table 4.3: Summary of number of nuclei in the 105-201 µm2 size range for the four viable and nonviable tumor samples.
Number of nuclei in each size range of the histogram was compared across the three regions of TTC uptake for the six liver samples. One tailed Student’s t test was performed to test for statistical differences in the number of nuclei for each size range between the groups. Use of one tailed Student’s t tests was justified by the fact that a reduction in number of nuclei in the peak population was expected. Figure 4.18 compares the mean and standard deviation for number of nuclei in three size ranges among viable, partially viable, and nonviable liver sections. It can be seen that number of nuclei in the 209-306 µm2 size range decreased from 293.3±115.5 for the viable group to 195.5±132.2 for the partially viable and 183.7±47.2 for the nonviable group.
Figure 4.19 shows the corresponding comparison for nonviable vs. viable tumor. It is seen that the nonviable tumor (3300±1201) group had fewer nuclei in the 105-201 µm2 size range in comparison to the viable tumor group (4607±311.5) per unit area of 1 mm2.
127 Viable liver 4000 Partially viable liver Nonviable liver
2000 16-113
0
2000
1000 113-209 0
500 209-306 0
Figure 4.18: Comparison of number of nuclei in three size ranges for viable, partially viable, and nonviable liver groups.
10000
5000 8-105
0
5000 105-201 0
4000
2000 201-298 0 Viable tumor Nonviable tumor Figure 4.19: Comparison of number of nuclei in three size ranges for viable and nonviable tumor groups.
128 Tables 4.4 and 4.5 list the t and p values for one tailed t tests comparing number of nuclei in three size ranges across the three TTC uptake regions for liver and tumor samples respectively.
For liver samples, the size bins compared were 16-113, 113-209 and 209-306 µm2. The difference in number of nuclei in all three size ranges between partially viable and nonviable sections of liver was not statistically significant and hence is not reported here. It is seen that number of nuclei in the 209-306 µm2 size range decreased significantly (t = −2.152, p = 0.0284) for the nonviable liver samples in comparison to viable liver samples.
For tumor samples, the size bins compared were 8-105, 105-201 and 201-298 µm2. The number of nuclei in the 105-201 µm2 size range decreased significantly (t = −2.1058, p = 0.0359) for the nonviable tumor in comparison to the viable tumor.
Size (µm2) Partially viable w.r.t Viable Nonviable w.r.t Viable 16-113 p = 0.3536 p = 0.2797 t = 0.3893 t = 0.6038 113-209 p = 0.0958 p = 0.2762 t = −1.4262 t = −0.6149 209-306 p = 0.1246 p = 0.0284 t = 1.2412 t = −2.152
Table 4.4: Student t-test statistics for comparing the number of nuclei in three size ranges among different TTC uptake regions in liver samples. Significant comparisons have been highlighted.
Size (µm2) Nonviable w.r.t Viable 8-105 p = 0.0869 t = −1.5424 105-201 p = 0.0399 t = −2.1058 201-298 p = 0.2046 t = −0.8869 Table 4.5: Student t-test statistics for comparing the number of nuclei in three size ranges among different TTC uptake regions in tumor samples. Significant comparisons have been highlighted.
129 4.4 Discussion These results indicate a decrease in the number of nuclei in the 209-306 µm2 size range for the nonviable liver samples and 105-201 µm2 size range for the nonviable tumor samples in comparison to the viable liver and tumor groups, respectively. No such statistically significant differences were seen while comparing the partially viable liver group to the viable liver group or while comparing partially viable liver group to nonviable liver group. It is hypothesized bulk ultrasound treatment causes liver cells in the 200-300 µm2 size range and tumor cells in the 100-
200 µm2 size range to disintegrate. It is interesting to note that the other size groups were not affected significantly.
Partially viable tissue marks the boundary of ablation where tissue temperatures and thermal dose were not as high as in the central ablated zone. Hence, the partially viable region does not show effects of treatment as prominently as in the nonviable region. This could be the reason for not obtaining significant statistical differences while comparing the partially viable region to the viable region. It is hypothesized that the cells in the partially viable region are possibly undergoing apoptosis or programmed cell death. Further staining for apoptosis such as propodium iodide (PI), which investigates the membrane integrity of the cell, may reveal the mechanism of cell death in the partially viable region.
While averaging six images to obtain average histograms for the tumor sample, it was seen that the histograms had large standard deviations within a single sample. It is known that tumor cells are quite different from liver cells. Tumor cells lose cell to cell recognition and are not regularly circular in shape. Hepatocytes are distinctly round in shape and arranged in lobules. For these reasons, it is easier to classify liver parenchyma viability than VX2 tumor viability using this method.
130 Based on these results, it is evident that different regions in a TTC stained section can be associated with varying degrees of cell death as seen in DAPI staining. Images from viable tissue, with round cells and normal sized nuclei, provide a baseline count for number of nuclei in the peak population size. When cells are subjected to bulk ultrasound ablation, they may typically disintegrate, hence decreasing the number of nuclei available in the peak population size ranges.
These results suggest that the three observed levels of TTC uptake are due to varying levels of cell death that can be histologically quantified. Hence, TTC staining can be used to evaluate tissue damage caused by bulk ultrasound ablation, by demarcating the boundary of ablation and as a surrogate for cellular histology changes corresponding to varying degrees of necrosis.
4.5 Conclusion
Correlation between TTC uptake and cellular histology has been illustrated in this chapter. The results indicate that with increasing cellular necrosis, TTC uptake decreases. From the DAPI sections, it was evident that partial TTC uptake corresponded to partial cellular death, which in living system would undergo programmed cell death. Regions of complete TTC uptake corresponded to healthy living cells. Regions of no TTC uptake corresponded to nonviable necrosed tissue and regions of partial TTC uptake corresponded to partially viable cells, which might probably undergo necrosis later. The results show that TTC can be used as an indicator for changes in cellular and nuclear morphology that could indicate cell death and hence can be used to evaluate efficiency of ultrasound ablation technique for liver and tumor treatments.
131 Chapter 5
Correlation of Passive Cavitation Images and Lesion Histology for in vivo Bulk Ultrasound Ablation
5.1 Objective
Bubble activity during HIFU ablation has been shown to render the treatment unpredictable by altering lesion size and depth (Chen 2003). Cavitation and tissue vaporization during ex vivo bulk ultrasound ablation, as characterized by quantifying acoustic emission signals captured by a passive cavitation detector (PCD), have been shown to predict lesion geometry including the area and depth of the treatment region (Chapter 2). Thus, the role of bubble activity in altering lesion histology for in vivo bulk ultrasound ablation is important to study. The results may help improve planning and monitoring of ultrasound bulk ablation for cancer treatment.
The objective of the experiments presented in this chapter was to evaluate the association of cavitation to lesion histology for in vivo bulk ultrasound ablation. Ultrasound bulk ablation was performed in vivo on swine liver using a focused ultrasound therapy transducer with focal intensity of about 2700-6000 W/cm2 for treatment duration of 20 s to 2 minutes. An ultrasound imaging array was used for passive cavitation imaging (Salgaonkar 2009a and 2009b). The ultrasound array was positioned to image at the focal plane of the therapy transducer. Use of a focused transducer ensures that cavitation activity is localized in a small region and hence easier to locate in the passive cavitation maps. The ultrasound exposures used in this chapter are similar to bulk ultrasound ablation because of the longer treatment time (about 2 minutes). After ablation,
132 the liver was harvested from the animal, frozen at −80 °C and sliced to identify the imaging/focal plane. The tissue slice corresponding to the imaging plane was stained with 2% TTC solution to evaluate lesion geometry at the focal plane.
Passive cavitation imaging has the ability to spatially and temporally resolve acoustic emission signals obtained from tissue ablation experiments. Spatially and temporally averaged acoustic emission signals were correlated with lesion geometry at the focal plane. Averaged emission levels were used as inputs to multiple regression analysis to build a model predicting lesion areas. Spatial maps of acoustic emissions were compared to lesion width in the focal plane using receiver operating characteristic (ROC) analysis.
5.2 Materials and methods
The experimental methods are described below.
5.2.1 Transducers
Figure 5.1 shows the overall setup and block diagram for experiments described in this chapter.
The figure shows the therapy transducer (IX-366, UTX Inc.), which is placed inside a transducer holder filled with degassed water, the imaging array transducer (L7, Ardent Sound, Mesa, AZ) and related electronics.
The therapy transducer used was a 4 MHz, 0.75 inch diameter, 1 inch focal length, spherically focused transducer with ultrasound intensity gain of 900 at the focus relative to transducer surface. Ultrasound intensity gain (G) at the focus was estimated from equation 5.1
(Zanelli 1993).
2 r 2 G , (5.1) F
133 where r is the radius of the transducer, λ is the wavelength and F is the focal length of the transducer.
The therapy transducer was driven by a signal generator and a power amplifier. The signal generator (33220A, Agilent, Santa Clara, CA) was set to generate a 4 MHz sinusoidal wave at
500 mV or 600 mV (peak to peak amplitude). The sinusoidal wave was amplified by a RF power amplifier (3100L, ENI, Bell Electronics, Kent, WA) with 50 dB gain. The acoustic power output from the therapy transducer was measured using a radiation force balance (Ohmic Instruments,
Easton, MD) for a range of input signal amplitudes.
The acoustic intensity at the surface of the transducer was estimated as the ratio of acoustic power and the active area of the transducer. The intensity at the focal point of the transducer was estimated by multiplying transducer surface intensity by the estimated focal intensity gain of 900.
The calculated focal intensities were used to plan signal generator settings for the experiments reported here. In these experiments, the therapy transducer with ultrasound intensity at the focus between 2700-6000 W/cm2 was powered continuously for treatment times between 20 seconds to
2 minutes.
The imaging transducer used was a 192 element linear array with a 7.5 MHz center frequency (L7, Ardent Sound, Mesa, AZ). The array with individual element size of 7∙0.195 mm2
(elevation∙azimuth), had a pitch of 0.22 mm. The array was focused in the elevation direction with an acoustic lens (focal length 25 mm). The imaging array transducer was connected to the
Iris imaging/therapy module described in Chapter 2 (Figure 2.3). The L7 imaging array was placed in the second transducer holder and held in place with screws (Figure 5.1b). Both the transducer holders were mechanically coupled on a steel rod and fastened by screws. The focal spot of the therapy transducer was centered in the plane of the imaging transducer and was 6.6
134 mm deep from the lower edge of the therapy transducer holder. The procedure to align the two transducers is described in the next section.
5.2.2 Aligning therapy and imaging transducers
The therapy and imaging transducers were aligned such that the L7 array transducer images the focal region of the therapy transducer. The imaging transducer was aligned with the therapy transducer through the use of a plastic cone. As shown in Figure 5.1a, the cone is placed inside the therapy transducer holder underneath the therapy transducer. The position of the L7 imaging array was adjusted such that the center of the cone was at the center of the B-scan image at a range of 2.75 cm from the array surface. The imaging array was then moved down, until the tip of the cone started disappearing from the B-scan image, to be aligned with the therapy transducer at the focal spot which is 0.6 mm below the tip of the cone. Figure 5.2 shows the plastic cone and the B-scan image of the alignment with the cone tip identified by the hyperecho circle. Following alignment, the cone was removed and the transducer holder containing the therapy transducer was sealed at the bottom using Tegaderm® and filled with degassed water. When the setup is positioned on the swine liver, the focal spot is 6.6 mm deep into the tissue (Figure 5.1b).
5.2.3 Experimental methods
Figure 5.3a shows the aligned transducer setup placed on a lobe of swine liver. It is seen that the liver tissue conforms to fill the space between the two transducers. Phosphate buffered saline
(PBS) was used as a coupling medium between the transducer and liver surface. Contact between the liver surface and the imaging array was confirmed by B-scan imaging.
For in vivo experiments, the animal’s abdomen was surgically opened and the liver was mobilized. The aligned transducer setup was positioned on the animal liver and held in place by hand (Figure 5.3b). A piece of absorbing rubber was placed at the underside of the liver under
135 the therapy transducer. A B-scan image of the liver was captured before the treatment. The data acquisition routine that captures the passive cavitation signals was synchronized to start with the start of the therapy.
5.2.3.1 Passive cavitation imaging
The theory behind passive cavitation imaging has been described by Salgaonkar (2009a, 2009b).
A linear ultrasound array (L7, Ardent Sound, Mesa, AZ) described earlier, was used to passively image the tissue during ultrasound ablation. The imaging array was operated under constant width subaperture with 5 focal zones covering 61 mm in the range direction. Images were captured at 28 Hz frame rate and the beamformed RF signals were recorded by a PC-based A/D card (Compuscope CS14200, Gage Applied, Montreal, Canada) at 33.3 MHz sampling frequency (Salgaonkar 2009a and 2009b). Nine frames of passive cavitation signals were recorded for each time instance.
Passive cavitation signals captured by the imaging array were later processed to spatially resolve acoustic emission signals. Passive cavitation imaging data was captured every 7 seconds throughout the experiment. At the end of the treatment, a B-scan image of the treated tissue was captured. Eight such experiments were performed. For three of the eight experiments, the frame trigger channel data, needed to identify the first line of passive cavitation imaging, was not captured. Hence, for these three experiments, the acoustic emission signals could not be spatially resolved but could be averaged spatially to obtain average acoustic emission levels similar to those described in Chapter 2. The focal spot of the therapy transducer was positioned at the last
(5th) focal zone of the B-scan image and hence passive cavitation signals from only the 5th focal zone were used for computing passive cavitation images.
136
IRIS hardware
a Tank filled
with water
Cone used for alignment Imaging transducer holder
b Agilent 3320A signal Mechanical coupling generator connecting the therapy ENI 3100L and imaging power amplifier transducers
4 MHz therapy Transducer transducer holder 25.4 mm
6.6 mm IRIS hardware Focal spot
Imaging transducer 27.5 mm 7.5 MHz holder Swine Liver Imaging array
Figure 5.1: Experimental block diagram showing therapy and imaging transducer aligned with the cone tip (a) and the setup for in vivo ablation (b).
137
Tip of the cone
Figure 5.2: B-scan (a) showing the tip of the cone for aligning ultrasound therapy and imaging
transducers and the cone (b).
5.2.4 Data processing
The 192 RF line signals captured by the imaging transducer were processed as described in
Salgaonkar 2009a & 2009b to spatially locate acoustic activity along the array azimuthal direction. The frequency spectrum of each RF line signal, with 828 data points from the fifth focal zone, was obtained using the periodogram method. Frequency spectra from the nine frames were averaged to obtain average frequency spectra. Figure 5.4 shows a representative frequency spectrum from one of the ultrasound ablation experiments.
138
Figure 5.3: Experimental setup showing aligned therapy and imaging transducers positioned on benchtop and on the animal liver.
139 PCI spectra
60
5 50
40 10
30 Time(s) 15 20
10
0 20 0 5 10 15 20 Frequency (MHz)
Figure 5.4: Representative frequency spectrum in dB scale from one of the in vivo experiments showing signal broadening at the fundamental (4 MHz) and first harmonic (8 MHz) frequency.
The brief temporal window employed leads to broadening of the passive cavitation signals in the frequency domain. Hence, to quantify energy at a particular frequency, energy in the frequency band encapsulating the frequency of interest was averaged. Energy in the 1.8-2.2 MHz and 3.8-4.2 MHz frequency bins was averaged to obtain energy at the subharmonic (2 MHz) and fundamental (4 MHz) frequencies, respectively. Spectral energy in the 7.8-8.2 MHz band was averaged to obtain energy at the first harmonic frequency (8 MHz). Energy in the 1.2-1.5 MHz band was averaged to quantify broadband emissions, as already described in section 2.3.
Spatially resolved acoustic emissions were averaged temporally to obtain spatial maps of time- averaged acoustic emissions. Spatially resolved acoustic emissions were averaged spatially and temporally, converted to dB levels based on the baseline noise floor spectrum level to obtain
140 averaged emission levels, equivalent to those recorded by a single element unfocused PCD as reported in section 2.5. Any low-frequency emissions recorded by the imaging transducer can not be spatially resolved due to the omnidirectional sensitivity of the array at low frequencies.
Hence, the passive cavitation imaging method could not reliably map low-frequency signals spatially, and thus low-frequency emissions were not analyzed in these experiments.
5.2.5 TTC staining
Following ablation, the liver tissue was cut into blocks encompassing the whole lesion and frozen at −80 °C with the correct orientation. The frozen blocks were then sliced parallel to the image plane. The tissue slice corresponding to the image plane was identified based on the slice thickness and position of the focal spot inside the tissue. The identified slice was stained with 2%
TTC solution for 45 minutes. The stained sections were scanned on a flatbed scanner
(CanonScan 8800F, Canon, NY) and the images were recorded at 300 or 800 dpi (dots per inch).
Figure 5.6 shows a representative section of tissue at the imaging plane with an evident focal lesion. The inner polygon outlines tissue with no TTC uptake and is identified as the ablated area
(AA). The outer polygon outlines tissue with partial or no TTC uptake and marks the treated area
(TA). Ablated and treated areas were quantified for all experiments using ImageJ (National
Institutes of Health, Bethesda, MD).
5.2.6 Statistical analysis
Spatially and temporally averaged subharmonic (SH), broadband (BB), fundamental (FM) and harmonic (HM) emission levels were cross correlated to lesion geometry including ablated area
(AA) and treated area (TA) as described in Chapter 2.
Multiple regression analysis was performed with the four averaged acoustic emission levels, namely SH, BB, FM and HM, as inputs to predict ablated and treated areas (AA and TA).
141 Equation 5.1 describes the multiple regression model used to predict ablated and treated area based on the four acoustic emission levels, where B0, B1, B2, B3 and B5 are regression coefficients corresponding to the intercept, subharmonic, broadband, fundamental and harmonic emissions respectively. The four inputs and the two outputs were standardized as explained earlier in section 2.4.
Y= B0 + B1∙ (SH) + B2∙ (BB) + B3∙ (FM) + B4∙ (HM) (5.1)
5.2.6.1 Receiver operating characteristic analysis
Receiver operating characteristic (ROC) analysis (Krzanowski 2009) was performed to compare spatial maps of acoustic emission levels to the lesion cross sections. Passive cavitation images were averaged over the length of treatment to obtain temporally averaged, azimuthally resolved acoustic emission levels for the four acoustic emission types namely subharmonic, broadband, fundamental and harmonic. Similar analysis for in vitro experiments has been performed earlier
(Salgaonkar 2009b).
TTC stained images were cropped in size to match the B-scan image dimensions (Figure 5.5).
The images were cropped with the center of the lesion at the center of the cropped image. TTC stained images with ablated and treated areas marked were converted to black and white thresholded images (Figure 5.5). Figure 5.5 shows the original TTC stained section and corresponding thresholded black and white images of ablated area and treated area. These thresholded images were summed across the range direction to obtain widths of ablated and treated regions along the range direction.
The one dimensional, spatially resolved, time averaged, acoustic emission signals were thresholded with varying thresholds. True positives (TP), true negatives (TN), false positives
(FP) and false negatives (FN) for predicting ablation and treatment in a region of tissue based on
142 the acoustic emission levels were calculated for a range of threshold values. To estimate the number of independent predictions for statistical analysis, numbers of negative and positive outcomes were scaled down according to the frequency-dependent azimuthal resolution of the imaging array. The focal plane intensity of the array at each aperture is assumed to be a sinc function (Salgaonkar 2009a, Mast 2007). Due to the finite width of the sinc function, the beams from adjacent apertures overlap and reduce the number of independent predictions of ultrasound lesion formation based on local acoustic emission levels. The intensity at a position (y) for given aperture size (b), azimuthal focus (z0), frequency (f) and sound speed (c) is given by equation 5.2
sin(x) 2 kby 2f I(y, z0 ) , where x and k (5.2) x z0 c
th The −6 dB point (y-6dB), where intensity falls to 1/4 of the peak energy was calculated for the array with aperture size of 7.04 mm, focus (z0) at 27.5 mm and sound speed of 1.54 mm/µs.
Resolution cell width (ψ) is calculated from equation 5.3.
2y6dB (5.3)
The effective number of independent predictions per image is then estimated as the ratio of total image width to resolution cell width. The resolution cell width and effective number of independent predictions are shown in Table 5.1 for the center frequencies of each frequency band considered. True positive rate and false positive rate were calculated for each threshold value as described in equations 5.4 and 5.5.
TP TPR (5.4) TP FN FP FPR (5.5) FP TN
143 The true positive rates (TPR) were plotted against the false positive rates (FPR) to obtain the
ROC curve. Area under the ROC curve (AUROC) was evaluated to estimate the efficiency of
acoustic emission levels in predicting ablation and treatment. AUROC value of 1 indicated an
ideal binary classifier. The closer the AUROC value is to 1, the closer the technique is to
performing as an ideal binary classifier. The obtained AUROC values were compared to a value
of 0.5 using equations described in Krzanowski (2009).
Frequency Center frequency (MHz) Resolution cell Effective number of band width (mm) independent predictions
Broadband 1.35 2.69 15.7
Subharmonic 2 1.81 23.3
Fundamental 4 0.91 46.6
Harmonic 8 0.45 93.1
Table 5.1: Resolution cell width (mm) and effective number of independent predictions per image at low-frequency, broadband, subharmonic, fundamental and harmonic frequencies.
144 a
Thresholded ablated area Thresholded treated area b 0 c 0
2 2
4 4
6 6
Range (mm)Range (mm)Range 8 8
10 10
12 12
-6 -4 -2 0 2 4 6 -6 -4 -2 0 2 4 6 Azimuth (mm) Azimuth (mm)
Figure 5.5: TTC stained image of tissue slice corresponding to the array imaging plane showing ablated area and treated area margins. Thresholded black and white image of ablated area (b) and treated area (c) for TTC stained section shown in Figure 5.5a.
5.3 Results
Table 5.2 lists the ultrasound intensities, treatment durations and energy delivered for each of the eight in vivo swine liver ablation experiments. Focal intensities were estimated as explained in section 5.2.1. Total energy delivered is the product of acoustic power (W) and time duration
(s).
145 Run number Acoustic power Intensity (W/cm2) Duration (s) Energy delivered (J)
output (W)
1 14 4050 120 1572
2 20.7 5940 50 940
3 14 4050 120 1572
4 14 4050 120 1572
5 9.4 2700 120 1092
6 9.4 2700 20 1092
7 9.4 2700 120 1092
8 9.4 2700 120 1092
Table 5.2: Ultrasound intensity, treatment duration and total energy delivered for in vivo ultrasound ablation of swine liver.
Figures 5.6 through 5.10 show representative passive cavitation maps created from fundamental, harmonic, subharmonic and broadband emissions for five in vivo ultrasound ablation trials. The images show increase in acoustic energy (dB) with respect to baseline (when ultrasound was off). For three (trials 1, 2 and 3) of the eight trials, frame trigger data was not captured and hence such passive cavitation images could not be formed for those trials. It is seen from Figures 5.6 through 5.10 that images of fundamental and harmonic emissions show energy localized towards the center of the array, where the focal spot of the therapy transducer was positioned.
146 Fundamental 4 MHz Harmonic 8 MHz 80 80 0.4 0.4 70 70 0.6 0.6 60 60 0.8 0.8 50 50 1 1
1.2 40 1.2 40
Time [s]Time [s]Time 1.4 30 1.4 30
1.6 20 1.6 20
1.8 10 1.8 10
2 0 2 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Subharmonic 2 MHz Broadband 1.2-1.5 MHz 5 5 0.4 0.4 4.5 4.5
0.6 4 0.6 4
0.8 3.5 0.8 3.5
1 3 1 3
1.2 2.5 1.2 2.5 Time [s]Time Time [s]Time 2 2 1.4 1.4 1.5 1.5 1.6 1.6 1 1 1.8 1.8 0.5 0.5
2 0 2 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Figure 5.6: Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband emissions for an in vivo ultrasound ablation experiment (Trial 4).
Fundamental 4 MHz Harmonic 8 MHz 80 80 0.4 0.4 70 70 0.6 0.6 60 60 0.8 0.8 50 50 1 1 40 40
1.2 1.2
Time [s]Time [s]Time 30 30 1.4 1.4 20 20 1.6 1.6 10 10 1.8 1.8 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Subharmonic 2 MHz Broadband 1.2-1.5 MHz 5 5 0.4 0.4 4.5 4.5
0.6 4 0.6 4
0.8 3.5 0.8 3.5
3 3 1 1 2.5 2.5
1.2 1.2 Time [s]Time Time [s]Time 2 2
1.4 1.5 1.4 1.5
1.6 1 1.6 1
1.8 0.5 1.8 0.5 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm] Figure 5.7: Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband emissions for in vivo ultrasound ablation experiment (Trial 5).
147 Fundamental 4 MHz Harmonic 8 MHz 80 80 0.34 0.34 70 70 0.36 0.36 60 60 0.38 0.38 50 50 0.4 0.4
40 40 Time [s]Time Time [s]Time 0.42 0.42 30 30 0.44 0.44 20 20 0.46 0.46 10 10 0.48 0.48 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Subharmonic 2 MHz Broadband 1.2-1.5 MHz 5 5 0.34 0.34 4.5 4.5
0.36 4 0.36 4
0.38 3.5 0.38 3.5 3 3 0.4 0.4
2.5 2.5 Time [s]Time Time [s]Time 0.42 0.42 2 2
0.44 1.5 0.44 1.5
1 1 0.46 0.46 0.5 0.5 0.48 0.48 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm] Figure 5.8: Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband emissions for in vivo ultrasound ablation experiment (Trial 6).
Fundamental 4 MHz Harmonic 8 MHz 80 80 0.4 0.4 70 70 0.6 0.6 60 60 0.8 0.8 50 50 1 1 40 40
1.2 1.2
Time [s]Time [s]Time 30 30 1.4 1.4 20 20 1.6 1.6 10 10 1.8 1.8 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Subharmonic 2 MHz Broadband 1.2-1.5 MHz 5 5 0.4 0.4 4.5 4.5
0.6 4 0.6 4
0.8 3.5 0.8 3.5
3 3 1 1 2.5 2.5
1.2 1.2 Time [s]Time Time [s]Time 2 2
1.4 1.5 1.4 1.5
1.6 1 1.6 1
1.8 0.5 1.8 0.5 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Figure 5.9: Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband emissions for in vivo ultrasound ablation experiment (Trial 7).
148 Fundamental 4 MHz Harmonic 8 MHz 80 80 0.4 0.4 70 70
0.6 60 0.6 60
50 50 0.8 0.8
40 40
Time [s]Time [s]Time 1 30 1 30
20 20 1.2 1.2
10 10 1.4 1.4 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm]
Subharmonic 2 MHz Broadband 1.2-1.5 MHz 5 5
0.4 4.5 0.4 4.5
4 4 0.6 0.6 3.5 3.5
3 3 0.8 0.8
2.5 2.5 Time [s]Time Time [s]Time 1 2 1 2
1.5 1.5
1.2 1 1.2 1
0.5 0.5 1.4 1.4 0 0 -20 -10 0 10 20 -20 -10 0 10 20 Azimuth [mm] Azimuth [mm] Figure 5.10: Passive cavitation maps of dB scaled fundamental, harmonic, subharmonic and broadband emissions for in vivo ultrasound ablation experiment (Trial 8).
Figures 5.11 through 5.13 show time traces of fundamental, harmonic, subharmonic, and broadband emissions determined from spatially averaged time-frequency spectra (Figure 5.4) for trials 4 through 8. Overall, it is seen that the experiments did not have large broadband and subharmonic emissions.
149 dB-scaled PCI emissions, trial 4, date 20100520 a 80 60 40 20 0 Fundamental (dB) Fundamental 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 80 60 40 20
Harmonic (dB) Harmonic 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
4
2
0 Subharmonic (dB) Subharmonic 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
4
2
Broadband (dB) Broadband 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Time (minutes)
dB-scaled PCI emissions, trial 2, date 20100826 b 80 60 40 20 0 Fundamental (dB) Fundamental 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 80 60 40 20
Harmonic (dB) Harmonic 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
4
2
0 Subharmonic (dB) Subharmonic 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
4
2
Broadband (dB) Broadband 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Time (minutes)
Figure 5.11: Spatially averaged time traces of fundamental, harmonic, subharmonic and broadband emission levels for trials 4 (a) and 5 (b).
150 dB-scaled PCI emissions, trial 4, date 20100826 a 80 60 40 20 0 Fundamental (dB) Fundamental 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 80 60 40 20
Harmonic (dB) Harmonic 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
4
2
0 Subharmonic (dB) Subharmonic 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8
4
2
Broadband (dB) Broadband 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 Time (minutes)
dB-scaled PCI emissions, trial 3, date 20100826 80 b 60 40 20 0 Fundamental (dB) Fundamental 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 80 60 40 20
Harmonic (dB) Harmonic 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
4
2
0 Subharmonic (dB) Subharmonic 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45
4
2
Broadband (dB) Broadband 0 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 Time (minutes)
Figure 5.12: Spatially averaged time traces of fundamental, harmonic, subharmonic and broadband emission levels for trials 6 (a) and 7 (b).
151 dB-scaled PCI emissions, trial 5, date 20100826 80 60 40 20 0 Fundamental (dB) Fundamental 0 0.2 0.4 0.6 0.8 1 1.2 1.4 80 60 40 20
Harmonic (dB) Harmonic 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4
4
2
0 Subharmonic (dB) Subharmonic 0 0.2 0.4 0.6 0.8 1 1.2 1.4
4
2
Broadband (dB) Broadband 0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Time (minutes)
Figure 5.13: Spatially averaged time traces of fundamental, harmonic, subharmonic and broadband emission levels for trial 8.
Trial 4 Trial 5 Trial 6
a b c
Azimuth
Trial 7 Trial 8
d e
Figure 5.14: Cropped sections of TTC stained tissue slices corresponding to the image plane for trials 4(a), 5(b), 6(c), 7(d) and 8(e) showing ablated (tan) and treated (pink and tan) areas.
152 Figure 5.14 shows cropped sections of TTC stained tissue slices corresponding to the image plane for trials 4 though 8. Tissue section size in the azimuthal direction corresponds to the length of the L7 imaging array in the azimuthal direction.
Acoustic emissions (dB) Fundamental Lesion width 90 Harmonic 8 Subharmonic Ablated area 80 Broadband 7 Treated area 70 6 60 5 50
40 4
30
3 Lesion width Lesion (mm)
Acoustic emissions (dB) 20 2 10 1 0
-10 0 -20 -15 -10 -5 0 5 10 15 20 -8 -6 -4 -2 0 2 4 6 8 Azimuth (mm) Azimuth (mm) Acoustic emissions (dB) Fundamental Lesion width 100 Harmonic 10 Subharmonic Ablated area Broadband 9 Treated area 80 8
7 60 6
40 5
4
20 width Lesion (mm)
Acoustic emissions (dB) 3
2 0 1
-20 0 -20 -15 -10 -5 0 5 10 15 20 -8 -6 -4 -2 0 2 4 6 8 Azimuth (mm) Azimuth (mm) Figure 5.15: Traces of azimuthally resolved acoustic emissions (left) and corresponding lesion width (right) from ablated and treated regions for trials 4 (top) and 5 (bottom).
153 Acoustic emissions (dB) Fundamental Lesion width 100 Harmonic 5 Subharmonic Ablated area Broadband 4.5 Treated area 80 4
3.5 60 3
40 2.5
2
20 width Lesion (mm)
Acoustic emissions (dB) 1.5
1 0 0.5
-20 0 -20 -15 -10 -5 0 5 10 15 20 -8 -6 -4 -2 0 2 4 6 8 Azimuth (mm) Azimuth (mm)
Acoustic emissions (dB) Fundamental Lesion width 90 Harmonic 8 Subharmonic Ablated area 80 Broadband 7 Treated area 70 6 60
50 5
40 4
30
3 Lesion width Lesion (mm)
Acoustic emissions (dB) 20 2 10 1 0
-10 0 -20 -15 -10 -5 0 5 10 15 20 -8 -6 -4 -2 0 2 4 6 8 Azimuth (mm) Azimuth (mm) Figure 5.16: Traces of azimuthally resolved acoustic emissions (left) and corresponding lesion width (right) from ablated and treated regions for trials 6 (top) and 7 (bottom).
Fundamental Acoustic emissions (dB) Lesion width 70 Harmonic 4 Subharmonic Ablated area 60 Broadband 3.5 Treated area
50 3
40 2.5
30 2
20 1.5
Lesion width Lesion (mm) Acoustic emissions (dB) 10 1
0 0.5
-10 0 -20 -15 -10 -5 0 5 10 15 20 -8 -6 -4 -2 0 2 4 6 8 Azimuth (mm) Azimuth (mm) Figure 5.17: Traces of azimuthally resolved acoustic emissions (left) and corresponding lesion width (right) from ablated and treated regions for trials 8.
154 Figures 5.15 through 5.17 show the temporally averaged one dimensional traces corresponding to the four acoustic emissions namely fundamental, harmonic, subharmonic, and broadband emissions (left panel) and ablated and treated region lesion widths (right panel) for trials 4 through 8. It is seen that the lesion is the widest at the center of the imaging array and tapers towards the edges of the imaging transducer. Energy in the subharmonic and broadband bins was very low compared to fundamental and harmonic emissions. The acoustic emissions were essentially the strongest at the center of the imaging array, where the focal spot was positioned.
Table 5.3 shows cross correlation coefficients between spatio-temporally averaged acoustic emission levels (SH, BB, FM and HM) and lesion areas (AA and TA) for 8 experiments.
Correlation coefficients (r) and probability values (p) are shown. It is seen that the correlation between fundamental emissions and the treated area is the highest and also statistically significant (p<0.05), while the lesion area did not correlate well with the other three acoustic emissions levels.
SH BB FM HM AA r = 0.3117 r = 0.2185 r = 0.5273 r = 0.5486 p = 0.4523 p = 0.6032 p = 0.1793 p = 0.1591 TA r = 0.4079 r = 0.2640 r = 0.7630 r = 0.6180 p = 0.3158 p = 0.5275 p = 0.0277 p = 0.1025 Table 5.3: Pearson’s cross-correlation matrix showing correlation coefficients (r) and probability values (p) for correlating acoustic emissions (SH, BB and LF) to lesion area (AA and TA) for in vivo bulk ultrasound ablation at 4 MHz.
Table 5.4 shows the results of the multivariate regression model in predicting ablated and treated area based on the four acoustic emission levels (SH, BB, FM and HM) for the eight ablation experiments. The table shows the regression coefficients B1, B2, B3 and B4 corresponding to subharmonic, broadband and fundamental and harmonic levels in predicting the two outputs; ablated area and treated area. From the table, it can be seen that the model was not reliable in
155 predicting ablated area. For predicting treated area, fundamental and broadband emissions were the only significant inputs, with the fundamental regressing positively and broadband emissions regressing negatively to the output. The low R2 value for treated area (23%) suggests that the model is not very reliable. It is to be noted that the broadband regression is only marginally significant.
2 SH (B1) BB (B2) FM (B3) HM (B4) p R AA −0.487 −0.837 0.496 0.579 0.6773 45.47% (p = 0.659) (p =0.464) (p = 0.297) (p =0.603) TA −1.545 −3.369 4.282 −0.497 0.0477 22.71% (p = 0.22) (p = 0.0434) (p = 0.0233) (p =0.6531) Table 5.4: Multivariate regression analysis showing regression coefficients and probability values for predicting ablated area and treated area based on average acoustic emission levels.
Subharmonic - Ablated area Subharmonic - Treated area 1 1
0.8 0.8
0.6 0.6
0.4 0.4 TruePositive Rate TruePositive Rate 0.2 0.2
0 0 0 0.5 1 0 0.5 1 False Positive Rate False Positive Rate
Broadband - Ablated area Broadband - Treated area 1 1
0.8 0.8
0.6 0.6
0.4 0.4 TruePositive Rate TruePositive Rate 0.2 0.2
0 0 0 0.5 1 0 0.5 1 False Positive Rate False Positive Rate Figure 5.18: ROC curves for classifying ablated and treated regions based on subharmonic and broadband emissions.
156 Figures 5.18 and 5.19 show ROC curves for predicting ablation and treatment regions based on the local acoustic emission (subharmonic, broadband, fundamental and harmonic) levels for the five ablation experiments in which acoustic emissions could be spatially resolved. It is seen that ROC curves for predicting lesion areas from harmonic emission levels were closest to an ideal binary classifier (AUROC = 1). The ROC curve for predicting tissue ablation and treatment based on fundamental energy levels did not perform as well as the harmonic emissions. The
ROC curves corresponding to subharmonic and broadband acoustic emissions did not show a statistically significant difference when compared to the AUROC of 0.5, suggesting that subharmonic and broadband emissions did not perform better than chance in predicting tissue ablation and treatment regions. Table 5.5 shows the area under ROC (AUROC) and corresponding probability (p) values for all the four emissions. It is seen that the AUROC for harmonic emissions are the highest at 0.63 for predicting ablated area and 0.59 for predicting treated area, followed by the AUROC for fundamental at 0.61 for predicting ablated area and
0.52 for predicting treated area. AUROC for predicting ablated area and treated area, based on subharmonic and broadband levels were less than 0.5. It is also seen that for predicting ablated area, fundamental and harmonic energy levels were the statistically significant inputs.
157
Fundamental - Ablated area Fundamental - Treated area 1 1
0.8 0.8
0.6 0.6
0.4 0.4 TruePositive Rate TruePositive Rate 0.2 0.2
0 0 0 0.5 1 0 0.5 1 False Positive Rate False Positive Rate
Harmonics - Ablated area Harmonics - Treated area 1 1
0.8 0.8
0.6 0.6
0.4 0.4 TruePositive Rate TruePositive Rate 0.2 0.2
0 0 0 0.5 1 0 0.5 1 False Positive Rate False Positive Rate Figure 5.19: ROC curves for classifying ablated and treated regions based on subharmonic and broadband emissions.
AUROC Subharmonic Broadband Fundamental Harmonic AA 0.4634 0.4255 0.6106 0.6339 p = 0.8175 p = 0.9412 p = 4.63*10-5 p = 6.8*10-12 TA 0.4588 0.3971 0.5197 0.5905 p = 0.5050 p = 0.5149 p = 0.4983 p = 0.4939 Table 5.5: Area under ROC (AUROC) curve and corresponding probability (p) values for classifying tissue ablation and treatment based on subharmonic, broadband, low-frequency and harmonic emission levels.
158 5.4 Discussion
The results reported here suggest that for the in vivo experiments reported here with relatively small subharmonic and broadband emissions, these two acoustic emissions did not correlate well with lesion areas and were not significant in predicting lesion area reliably.
Harmonic emissions, produced due to scattering of ultrasound energy by tissue and bubbles, were significantly high in these in vivo experiments. The harmonic emissions were also a good classifier of tissue ablation and treatment. Hence, it can be said that scattering of ultrasound energy by tissue can be used to monitor ultrasound treatments and to predict ablated and treated regions in the tissue. Ultrasound energy at the fundamental frequency can be scattered by tissue structures and bubble clouds present intrinsically in the tissue. In these experiments, the fundamental energy correlated well with treated area and was also significant in predicting treated area. It was expected for fundamental energy to correlate to lesion geometry.
It is important to note that AUROC values for predicting tissue ablation and treatment based on harmonic emission levels were slightly higher than AUROC values predicting tissue ablation and treatment based on fundamental energy levels. The results suggest that harmonic energy was a better predictor of tissue ablation and treatment in comparison to fundamental energy. This result was consistent with previous passive cavitation imaging performed for in vitro HIFU liver ablation (Salgaonkar 2009a) where it was shown that harmonic scattering from vapor or gas bubbles may predict lesion formation.
Based on the multiple regression results, it was evident that the acoustic emissions were better at predicting treated area than ablated area. This result is consistent with multiple regression results from the ex vivo experiments reported in chapter 2 (Tables 2.4 and 2.8).
159 Similar passive cavitation mapping experiments done on ex vivo bovine tissue showed about a 10 dB rise in broadband emissions and a 20 dB rise in harmonic emissions locally for treatment conditions of 2000 W/cm2, 30 sec at 1.1 MHz (Salgaonkar 2009b). For the in vivo experiments reported here, with acoustic intensity levels comparable to the previous ex vivo experiments (Salgaonkar et. al 2009b), acoustic emission levels were lower (Figure 5.11 through
5.13) in comparison to the ex vivo experiments. Possible reasons for decrease in cavitation and bubble activity could be an increase in cavitation thresholds in vivo, and at 4 MHz as opposed to at 1.1 MHz. A 2 minute treatment time in vivo may not have been enough to cause significant tissue vaporization, as seen in ex vivo experiments reported in Chapter 2 with 10 or 20 minute treatment times. Cavitation thresholds for in vivo tissue are known to be higher than for ex vivo tissue due to scarcity of free bubbles in living tissue (Barnett 1998). Due to the nonavailability of a free bubble population in vivo, it takes more energy to build bubble populations in comparison to ex vivo tissue.
Registering frozen and thawed tissue to passive cavitation maps could introduce error in
ROC analysis, and hence it is important to estimate the amount of error introduced by freezing and thawing the tissue. Human liver tissue, when frozen at −80 °C for 24 hours and thawed later, shows a 5% increase in extracellular matrix area, possibly due to cell shrinking (Schäfer 1999).
While thawing frozen tissue, any cells that had shrunk may swell to their original size by water influx. Cells that were dead before freezing will not reach their prior volume (Schäfer 1999).
Based on Schäfer et. al (1999) freezing and thawing may cause about 5% shrinkage in tissue volume, corresponding approximately to a 3.4% decrease in lesion area and a 1.7% decrease in lesion depth.
160 For the experiments reported in this chapter, tissue slices were registered with passive cavitation maps by overlaying the center of the tissue lesion to the center of the passive cavitation maps, and hence to the center of the imaging array. Two kinds of errors could result from this experimental setup. The first error could arise from the misalignment of the cone tip with the center of the imaging array. The second error could arise from the assumption that the lesion is centered on the transducer focus. Alignment of the cone tip with the center of the array was confirmed by B scan imaging before the start of each experiment. Based on B scan images of the cone tip, displacement of the cone inside the therapy transducer holder can cause a ±1 mm error in the registration of a passive cavitation image with the tissue section. Assuming that the lesion is centered on the transducer focus could introduce error in registration of the tissue with the passive images. It is possible that the presence of blood vessels in the focal plane may cause uneven deposition of energy and hence shift the center of the lesion away from the focus. For the experiments reported here, all the lesions were devoid of any large blood vessels in the focal plane. The presence of radially symmetric lesions and the absence of blood vessels ensure that the lesions were centered on the transducer focus.
5.5 Conclusion
For the experiments reported here, subharmonic and broadband emissions were not strong enough to affect lesion geometry and hence effect of subharmonic and broadband emissions on lesion geometry could not be evaluated. Harmonic emissions, caused by nonlinear scattering of ultrasound energy by bubbles in the tissue, may be an important tool to monitor ultrasound ablation treatments in vivo, where cavitation activity is drastically reduced due to increased cavitation thresholds. Harmonic emissions may be caused due to nonlinear scattering of
161 ultrasound energy by tissue structures and bubble population present in the treatment zone.
Hence, for treatments where there is no significant cavitation activity, harmonic emissions can provide us with monitoring options. For future studies, it is important to monitor harmonic emissions in addition to subharmonic, broadband and low-frequency emissions.
162 Chapter 6
Conclusions and Future Work
6.1 Conclusions
In this thesis, the role of cavitation and tissue vaporization in altering thermal dose and tissue histology was evaluated for ex vivo and in vivo ultrasound ablation. Overpressure was used to suppress cavitation and tissue vaporization in ex vivo experiments. Statistical models were built for predicting lesion areas and depths based on measured acoustic emission levels for the ex vivo and in vivo experiments. TTC uptake of ultrasound treated and thermally heated tissue with similar thermal doses was compared. Logistic regression models were built to predict the probability of tissue coagulation based on thermal dose levels, and compared for the ultrasound and water bath heating conditions. DAPI histology in three TTC uptake regions of bulk ultrasound treated tissue was studied and quantified. For in vivo bulk ultrasound ablation experiments, passive cavitation imaging was used to map cavitation activity and compared to lesion formation using ROC analysis.
In Chapter 2, the role of cavitation and tissue vaporization in ex vivo bulk ultrasound ablation was studied by suppressing cavitation through the use of overpressure. It was found that at the lower frequency (3.1 MHz), with no significant tissue vaporization, subharmonic emissions regressed positively to lesion areas. At 4.8 MHz, with significant tissue vaporization, subharmonic and low-frequency emissions regressed negatively to lesion areas and depths. It can be concluded that for bulk ultrasound ablation, stable cavitation may increase lesion area by increasing heat deposition, while tissue vaporization may decrease lesion area and depth by
163 shielding ultrasound energy from the treatment zone. The new bubble population created by tissue vaporization may create cavitation nuclei and increase cavitation activity. A limitation of using overpressure to suppress cavitation was to raise the tissue boiling point and delay tissue vaporization. In addition to suppressing cavitation, applying overpressure delays the availability of new cavitation nuclei by delaying tissue vaporization, leading to decreased acoustic emission levels in the overpressure experiments. It is also possible that applying overpressure alters the bubble population in the medium, affecting cavitation activity. For the experiments reported here, overpressure affects both cavitation and tissue vaporization and hence it was difficult to distinguish the role of cavitation and tissue vaporization in lesion formation. For future experiments, it is suggested to alter available bubble populations by introducing ultrasound contrast agents into the medium to increase cavitation activity without affecting tissue vaporization.
The role of cavitation was opposing for experiments at the two frequencies 3.1 MHz and
4.8 MHz. The differences in tissue absorption, cavitation threshold and tissue vaporization at the two frequencies further complicated data interpretation. Future studies should be designed to include knowledge of initial bubble populations, in addition to passive cavitation detection. The use of ultrasound contrast agents can help in this regard, as they can be characterized and would give an idea of available initial bubble sizes and population when infused in the medium.
Ultrasound contrast agents with characterized sizes can be injected into the animal before treatment. The addition of bubble population measurement to the in vivo ultrasound ablation experiments would simplify interpretation of data and would help us extend the results to a range of frequencies and operating conditions.
164 Experiments reported in chapter 3 were designed to compare tissue viability between ultrasound treated and water bath heated tissue while maintaining thermal dose comparable between the two experiments. The results suggest that the threshold for tissue coagulation may be lower for ultrasound experiments in comparison to the water bath experiments with comparable thermal dose. However, this decrease in tissue coagulation threshold was not statistically significant. The results suggest that cavitation may increase lesion area by increasing heat deposition, which in turn may increase thermal bioeffects. It was seen that thermal doses from ultrasound treated tissues could only be replicated with a fair amount of error. The water- bath experiments were designed to match the base 10 logarithm of the thermal dose (log10 TD) and not the thermal dose itself. An accuracy of ±0.5 in log10 TD was considered sufficient to compare the tissue viability between the two experiments. It is to be noted that a difference of
±0.5 in log10 TD translates to a multiplicative factor of about 3 in thermal dose between the ultrasound and water bath experiments. It is recommended that future studies should be designed to replicate thermal dose from the ultrasound experiments very closely. Computer-controlled heaters, which can replicate a preprogrammed heating curve, can be used to replicate ultrasound heating curves closely and hence match the thermal doses with much higher accuracy.
Results reported in chapter 4, comparing the cellular histology of tissue for levels of TTC uptake, confirmed that cellular histology changed between complete, partial and no TTC uptake regions. It was also shown that the histological effects can be quantified by changes in the nuclear size distribution. Tissue regions with no TTC uptake showed changes in nuclear population that are hypothesized to be due to nuclear disintegration. Tissue regions with partial
TTC uptake showed histological changes that might correspond to tissue undergoing apoptosis.
Apoptosis or programmed cell death occurs when tissue has undergone irreversible damage, but
165 has not been damaged enough to cause necrosis immediately. To test for apoptosis in this region, it is recommended to perform TUNEL staining. Addition of apoptotic staining to quantify histology in the partial TTC uptake region will elucidate the mechanism of cellular damage that leads to partial TTC uptake.
Results from chapter 5, correlating passive cavitation images to lesion histology, were inconclusive in elucidating the role of cavitation in altering lesion geometry during in vivo bulk ultrasound ablation. The lack of strong cavitation signals during these in vivo ultrasound experiments may be the cause of non-significant correlations between acoustic emissions and lesion dimensions. Similar ex vivo experiments by Salgaonkar (2009b) have shown that cavitation can be mapped and be used to predict local lesion formation for ultrasound ablation.
For future studies, it is suggested to employ ultrasound contrast agents to encourage the occurrence of significant cavitation activity in the experiments. Results from Chapter 6 suggest that harmonic emissions can be mapped to monitor bulk ultrasound ablation for in vivo experiments where cavitation activity is minimal or absent.
The methods and results reported in this thesis helped confirm the two central hypotheses stated in chapter 1. It was confirmed that cavitation plays a significant role in altering tissue necrosis for bulk ultrasound ablation, and that cavitation increases heat deposition locally, hence increasing thermal dose locally in the tissue. It was also confirmed that TTC uptake correlates to thermal dose and cellular histology in thermally heated tissue.
6.2 Future Work
The experiments and results reported in this thesis may serve as investigatory studies toward the final goal of developing a cavitation-based monitoring method to predict success of ultrasound ablation in real time. For achieving the above goal, two questions need to be answered, namely
166 (1) does cavitation affect ablation outcome in bulk ultrasound ablation? and (2) can cavitation predict tissue ablation outcome in ultrasound ablation? These questions have been answered in this thesis. It was found that cavitation affects the outcome of ultrasound bulk ablation and that harmonic emissions can be spatially mapped to predict tissue treatment. Although it has been shown that cavitation can be mapped and used to predict lesion formation for ex vivo ultrasound ablation (Salgaonkar 2009b), further studies need to be performed to confirm the possibility of predicting tissue ablation based on cavitation maps for in vivo ultrasound ablation.
The logical next steps to follow would be to (1) investigate the feasibility of manipulating cavitation to manipulate and obtain a desired treatment region and (2) to validate passive cavitation imaging for predicting lesion formation during in vivo ultrasound ablation.
To investigate the feasibility of controlling cavitation to obtain a desired treatment region, it is suggested to employ ultrasound contrast agents to seed controlled cavitation. The amount of cavitation can be manipulated by manipulating the concentration and bubble population of ultrasound contrast agents such as Definity®. It is suggested that these experiments be performed in vivo. The scarcity of free bubbles in the living tissue will aid in producing reproducible cavitation activity for given amounts of ultrasound contrast agents. Reproducibility of obtaining desired treatment regions for given bubble populations and sonication parameters should be evaluated rigorously. Upon careful study of association of seeded cavitation to treated regions, ultrasound contrast agents can be injected in living tissue to seed cavitation and control the treatment area and depth. A decision making algorithm that can decide on the initial bubble population and amount of ultrasound contrast agent needed for a desired treatment area and depth would be useful clinically.
167 To validate passive cavitation imaging as a means to predict local lesion formation, it is suggested to further investigate the association of cavitation activity to tissue ablation for in vivo bulk ultrasound ablation in the presence of contrast agents. ROC analysis can be used to predict the threshold of cavitation for predicting tissue ablation. Such thresholds should be estimated and rigorously tested for various experimental conditions. The thresholds could be used to indicate treatment success locally and serve as a treatment end point indicator to the surgeon. Special care should be taken to adjust the threshold locally for presence of blood vessels in the treatment zone that can carry heat away from the treatment region.
Another important goal of the current study was to evaluate the histology of bulk ultrasound ablated tissue. It was shown that cavitation can be used to predict tissue coagulation, which in turn affects TTC uptake and can be associated with local nuclear histology. Developing a method to help visualize tissue histology based on local cavitation during treatment can be useful in providing the surgeon with real-time feedback of effect of treatment on tissue histology and help make decisions regarding treatment end points. It is hypothesized that most of the cells in the region of partial TTC uptake will undergo apoptosis. It is also possible that a portion of cells that did not acquire enough thermal insult may repair themselves. This could lead to a reduction in the treatment margin, and in the case of tumor treatment, to incomplete ablation of tumor and increased possibility of tumor recurrence. It is hence suggested to perform survival studies to evaluate the time-dependent changes in treatment margins, using standard histology techniques.
Result from these experiments should be useful in planning a treatment region that covers the tumor along with an appropriate safety margin.
Given below is a summary of suggested future directions for the project.
168 1. Possibility of seeding cavitation to increase bulk ablation rates and to produce controlled
bulk ultrasound ablation in tissue.
2. Validating passive cavitation imaging to predict lesion formation for in vivo ultrasound
ablation.
3. Use of knowledge on the histology of ultrasound-treated tissue to plan ablation treatments
in order to optimize treatment margins.
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177 Appendix
Tissue processing and staining protocols
A. TTC staining protocol
TTC staining protocol used to stain tissue described in section 2.2.8 is described below. TTC staining was used to delineate treatment boundary and to help distinguish nonviable and partially viable tissue for the overpressure experiments in Chapter 2 and in vivo ultrasound ablation experiments in Chapter 5.
Dissolve 2g of TTC salt (Fisher Scientific, St. Louis, MO) in 100 ml of 0.1 M PBS to prepare 2% TTC solution. Place tissue slice to be stained in the solution for 45 minutes.
B. Tissue processing protocol
Tissue processing protocol, used in preparing tissue in section 4.2.3 before H&E and DAPI staining is described below. Tissue blocks (1 cm∙1 cm∙0.5 cm) were processed, embedded in wax and sectioned for H&E and DAPI staining.
1. 24-48 hours in 10% neutral buffered formalin
2. 45 minutes in 70% alcohol (ETOH)
3. 45 minutes in 80% ETOH
4. 45 minutes in 95% ETOH
5. 45 minutes in 100% ETOH
6. 60 minutes in 100% ETOH
7. 60 minutes in 100% ETOH (second change)
178 8. 60 minutes in clearing reagent (xylene or substitute)
9. 60 minutes in clearing reagent (xylene or substitute, second change)
10. Embed in tissue cassettes using paraffin wax.
C. Hematoxylin and Eosin staining protocol
Hematoxylin and eosin staining protocol used to process tissue as described in section 4.2.3, to evaluate histology of in vivo ultrasound ablated tissue, is described below.
Deparaffinize and rehydrate sections:
1. 3 x 3 minutes in xylene (blot excess xylene before immersing into ethanol)
2. 3 x 3 minutes in 100% ETOH
3. 1 x 3 minutes in 95% ETOH
4. 1 x 3 minutes in 80% ETOH
5. 1 x 5 minutes de-ionized water (blot excess water from slide holder before immersing
into hematoxylin)
Hematoxylin staining:
6. 1 x 1.5 minutes in hematoxylin
7. Rinse in de-ionized water
8. 1 x 5 minute rinse in tap water (allow stain to develop)
9. Dip 8-12 times (fast) in acid ethanol (to de-stain)
10. Rinse 2 x 1 minute in tap water
11. Rinse 1 x 2 minutes in de-ionized water (can leave overnight at this stage).
12. Blot excess water from slide holder before immersing into eosin.
179 Eosin staining and dehydration:
13. 1 x 30 seconds in eosin (up to 45 seconds for an older batch of eosin)
14. 3 x 5 minutes in 95% ETOH
15. 3 x 5 minutes in 100% ETOH (blot excess ethanol before immersing into xylene)
16. 3 x 15 minutes in xylene
Can leave slides in xylene overnight to dehydrate any remaining water.
17. Coverslip slides using Permount (xylene based mounting media). Place a drop of
Permount on the slide using a glass rod, taking care to leave no bubbles. Angle the coverslip
and let fall gently onto the slide. Allow the Permount to spread beneath the coverslip,
covering whole of the sample. Dry the slide overnight in the hood before taking images.
D. DAPI staining protocol
DAPI staining protocol used for staining tissue described in section 4.2.3 is described below.
DAPI staining was used to evaluate nuclear histology of TTC uptake regions seen in in vivo ultrasound ablated tissue.
1. Bake slides at 56-58 °C for 2 hours
2. 3 x 15 minutes in xylene
3. 1 x 5 minutes in 100% ethyl alcohol (ETOH)
4. 1 x 1 minute in 100% ETOH
5. 2 x 30 seconds in 95% ETOH
6. 2 x 30 seconds in 70% ETOH
7. 2 x 5 minute washes in de-ionized water
180 8. 1 x 45 minute wash in 0.1% triton in PBS. Put 0.1% triton in a Coplin jar, put the slides
in the slots inside the jar and put the jar on the shaker with low setting.
9. 45 seconds in 0.1 µg/ml DAPI solution (1:100 dilution of 10µg/ml DAPI in 0.1 M PBS).
10. 3 x 2 minutes PBS washes
11. Wipe excess PBS from the slide. Add water based mounting media (Vector Laboratories),
to the slide and coverslip.
Bypass steps 10 and 11 if using anti-fade mounting media with DAPI (Vector Laboratories). Just add a drop of the anti-fade mounting media and coverslip.
181