Terahertz Spectroscopic Characterization and Imaging for Biomedical Applications

DISSERTATION

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Woon-Gi Yeo, B.S., M.S.

Graduate Program in Electrical and Computer Science

The Ohio State University

2015

Dissertation Committee:

Kubilay Sertel, Advisor

Fernando Teixeira

Ümit V. Ҫatalyürek

Niru K. Nahar

Woon-Gi Yeo

2015

Abstract

THz-frequency spectroscopic imaging has recently drawn increasing attention as a novel modality for bio-medical analysis of diseases and conditions of living tissues. More importantly, detection of cancerous tumors as well as necrotic tissue regions is being studied using THz waves with the aim of translating research studies into clinical practice. THz radiation provides unique sensing capabilities applicable to a variety of areas including non-destructive inspection, security screening, as well as bio-medical imaging. THz waves are safe (non-ionizing), and they can provide high-resolution with better specificity compared to X-rays. In addition, THz waves enable the spectroscopic analysis of organic molecules, since many of their rotational and vibrational resonances fall within the THz band.

Perhaps more importantly, THz waves are extremely sensitive to the degree of sample hydration and this property has been utilized to differentiate cancerous tissue regions. However, previous studies on human tissue groups have been largely disconnected, with publications focusing on only limited tissue groups at a time. In addition, assessment of cancer margins to differentiate in-situ extent of disease has rarely been a major focus. As such, a more general in-depth study of the THz response of extended human tissue groups is much desired to demonstrate the potential of THz sensing as a clinical tool.

In this work, we initially focus on a comprehensive experimental study of the THz response of major human tissue malignancies to investigate the efficacy of THz sensing as a clinical bio-medical tool. In particular, using the THz-band spectroscopic reflectivity and transmission properties of bulk and thin tissue samples, we characterize the material properties, such as index of refraction and material loss associated with the corresponding tissue characteristics. To do so, we develop accurate calibration techniques to take into account and eliminate experimental fixture effects. In addition, the specificity and sensitivity of the commercial time-domain THz spectroscopy system is quantified using bio-chemical compounds with known spectroscopic response.

Subsequently, the sensitivity of THz waves to tissue hydration is quantified through several freshly-excised tissue samples. Based on this study, we demonstrate that cancer margins can also be accurately characterized using the differences in tissue hydration.

The study is then expanded to include human lung and small intestine tissues with malignant regions. New image processing algorithms are developed to enhance THz image contrast and localization of malignant areas. Furthermore, a THz polarimetric sensing scheme is introduced to increase sensitivity of cancer margin identification and its performance is demonstrated through full-wave simulations.

Finally, human brain tissues exhibiting Alzheimer’s disease are investigated to demonstrate the utility of THz imaging in Alzheimer’s detection. We quantify, for the first time, measurable differences in THz reflectivity of gray and white brain matter, leading to accurate post-mortem diagnosis of Alzheimer’s disease. Furthermore, we hypothesize that the reason for the reflectivity contrast is due to demyelination of axons.

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To support this hypothesis, we present full-wave electromagnetic simulations of a simplified axon model and compare the simulation data with THz reflectivity measurements. Although this initial study demonstrates the efficacy of THz sensing in

Alzheimer’s detection, more case studies are needed to establish this finding as a viable clinical biomarker in the detection of Alzheimer’s disease in early stage.

This is dedicated to my wife Hyosoon, lovely sons Ian & Ethan, parents Gonghyun & Okhee, and younger brother, Woonsung.

Acknowledgments

This work could not have been completed without the heartfelt advice and the considerate support of many people around me. First and foremost, I would like to express the deepest gratitude to my advisor, Prof. Kubilay Sertel, and to my supervisor,

Dr. Niru K. Nahar. Their continuous guidance, patience, and encouragement in study as well as in life throughout years are invaluable and are compelled sincere thanks of me.

Considering how important it is to have a good advisor for Ph.D. study, I feel very fortunate that I have worked with such morally and professionally wonderful people like

Prof. Sertel and Dr. Nahar. I would also like to thank my previous advisors, Prof. John L.

Volakis and Prof. Robert Lee, for accepting me in the Ph.D. program and guiding me to be successful in early Ph.D. study. Without their supports, this work was not even started.

In addition, I am grateful to Prof. Ümit V. Çatalyürek and Prof. Fernando L. Teixeira for serving on the committee for my Ph.D. candidacy exam and dissertation defense. They not only took time off from their busy schedule but also kindly guided me to complete this work.

It has been a great experience to meet and become good friends with many colleagues at ElectroScience Laboratory (ESL). For their valuable discussion, motivation, and friendship, I really want to give special thanks to Dr. Georgios Trichopoulos, Dr.

Yasir Karisan, Cosan Caglaran, Dr. William Moulder, Varittha Sanphuang, Dr. Elian

Alwan, Dr. Nil Apaydin, Henry Vo, Rey Febo, Anas Abumunshar, Seckin Sahin, Syed

An Nazmus. I would also like to thank both present and past Korean friends at ESL for their emotional support, valuable discussion, and the useful consultation of my past, present, and future plans: Hak-Su Moon, Seung-Ho Doo, Dr. Gil Young Lee, Dr. Chun-

Sik Chae, Dr. James Park, Dr. Jun Seok Lee, Dr. Kuem Su Song, Dr. Joonshik Kim, Dr.

Jae-Young Chung. Dr. Youngchel Kim, Jeonghwan Park, Dongyeop Na. In addition, I would like to acknowledge a favor of my friends out of ESL and the acquaintances who know through research projects: especially, Dr. Sangjo Choi, Dr. Jungsuek Oh, Dr.

Wonbin Hong, Dr. Ogan Gurel, Richard Higgins, Scott Yano, Dr. Don Burdette, Dr.

David Daughton, the late Dr. Gilbert E. Pacey, Dr. Phil Taday, Rebecca Goodall, Dr.

Robert May, Dr. Ian Gregory, James McGilp.

Finally, I would like to thank my family. The unconditional love and support of my parents and mother-in-law have been the greatest motivation during the graduate study.

Most importantly, I would like to express the deepest gratitude to my wife, Hyosoon

Kim, for the full material and emotional support. Just the mere existence of her has given me full healing in the graduate life. I also thank to my two sons, Ian Yeo and Ethan Yeo.

You are all of my pleasures and hopes. I love you!

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Vita

July 14, 1981 ...... Born – Namwon, Jeollabuk-do, Korea

February, 2007 ...... B.E. Electronics, Telecommunication, and

Computer Eng., Korea Aerospace University

May, 2010 ...... M.S. Electrical Eng.,

University of Michigan

August, 2014 ...... M.S. Electrical and Computer Eng.,

The Ohio State University

2010 to present ...... Graduate Research Associate,

ElectroScience Laboratory, Department of

Electrical and Computer Engineering,

The Ohio State University

Publications

Journal Publications

W.-G. Yeo, N. K. Nahar, and K. Sertel, “Far-IR Multi-Band Dual-Polarization Perfect Absorber for Wide-Incident Angles,” Microwave and Optical Technology Letter, 55(3), pp. 632-636, January 2013.

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V. Sanphuang, W.-G. Yeo, J. L. Volakis, and N. K. Nahar, “THz Transparent Metamaterials for Enhanced Spectroscopic and Imaging Measurement,” IEEE Transaction on Terahertz Science and Technology, 5(1), pp. 117-123, January 2015.

W.-G. Yeo and N. K. Nahar, “Characterization of a THz CW spectrometer pumped at 1550nm,” Infrared Physics and Technology, vol. 71, pp. 70-76, July 2015.

W.-G. Yeo, N. K. Nahar, C. L. Hitchcock, S. Park, O. Gurel, and K. Sertel, “THz Imaging of Human Lung and Small Intestine Tissues for Cancer Margin Assessment,” Submitted to IEEE Transaction on Terahertz Science and Technology.

Conference Publications (selected)

W.-G. Yeo, T.-Y. Seo, J. W. Lee, and C. S. Cho, “H-Plane Sectoral Filtering Horn Antenna in PCB Substrates Using Via Fences at Millimeter-Wave,” in European Microwave Conference (EuMC), Munich, Germany, October 2007.

W.-G. Yeo, N. K. Nahar, R. Lee, and J. L. Volakis, “New frontiers for commercial applications of terahertz,” in IEEE National Aerospace and Electronics Conference (NAECON), Dayton, Ohio, USA, July 2011.

W.-G. Yeo, N. K. Nahar, and J. L. Volakis, “Validation of CW THz Spectral Measurements,” in IEEE International Symposium on Antennas and Propagation and USNC-URSI National Radio Science Meeting, Chicago, Illinois, USA, July 2012.

W.-G. Yeo, N. K. Nahar, and K. Sertel, “Dual-Band, Wide-Incident-Angle Absorber for Far-IR and THz Frequencies,” in IEEE National Aerospace and Electronics Conference (NAECON), Dayton, Ohio, USA, July 2012.

W.-G. Yeo, V. Snaphuang, N. K. Nahar, and John L. Volakis, “THz Periodic Surfaces to Enhance Spectroscopic Measurements,” in International Conference on Electromagnetics in Advanced Applications (ICEAA), Cape Town, South Africa, September 2012.

W.-G. Yeo, N. K. Nahar, and K. Sertel, “Phased Array Antenna with Integrated MEMS Phase Shifters for Ka-Band SATCOM,” in IEEE International Symposium on Antennas and Propagation and USNC-URSI National Radio Science Meeting, Orlando, Florida, USA, July 2013.

W.-G. Yeo, N. K. Nahar, C. L. Hitchcock, O. Gurel, S. Park, and K. Sertel, “Real-time THz Imaging of Human Tissue Characteristics and Cancer Margins,” in the 38th

International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz), Mainz, German, September 2013.

W.-G. Yeo, N. K. Nahar, C. L. Hitchcock, S. Park, O. Gurel, and K. Sertel, “THz Spectroscopy and Imaging of Major Human Organ Tissues for Cancer Margin Assessment,” in IEEE International Symposium on Antennas and Propagation and USNC-URSI National Radio Science Meeting, Memphis, Tennessee, USA, July 2014.

W.-G. Yeo, O. Gurel, N. K. Nahar, C. L. Hitchcock, N. L. Lehman, S. Park, and K. Sertel, “THz Imaging of Alzheimer’s Disease: Spectroscopic Differentiation between Normal and Diseased Tissues,” in the 39th International Conference on Infrared, Millimeter and Terahertz Waves (IRMMW-THz), Tucson, Arizona, USA, September 2014.

Fields of Study

Major Field: Electrical and Computer Engineering

Specialization:

Electromagnetics Antennas Metamaterials Biomedical Sensing

Table of Contents

Abstract ...... ii

Dedication ...... v

Acknowledgments...... vi

Vita ...... viii

List of Tables ...... xv

List of Figures ...... xvi

Chapter 1: Introduction ...... 1

1.1 THz Waves and Their Applications in Spectroscopy and Imaging ...... 2

1.2 THz Spectroscopic Imaging in a Bio-medical Tool ...... 5

1.3 Major Contributions of the Dissertation ...... 9

1.4 Dissertation Overview ...... 11

Chapter 2: Fundamentals of THz Spectroscopy ...... 14

2.1 Generation and Detection Schemes of THz Signal ...... 15

2.1.1 Photoconductive Antenna ...... 15

2.1.2 Photomixing ...... 18

2.1.3 Schottky Diode Frequency Multiplier Chain ...... 20 xi

2.2 Post-processing of Raw THz Spectroscopy ...... 22

2.2.1 Time Domain Spectrometer ...... 23

2.2.2 Continuous Wave Spectrometer ...... 28

2.3 Comparison of State-of-the-art THz Spectroscopic Systems ...... 32

2.4 Validation of THz Spectroscopy ...... 34

Chapter 3: Spectroscopic Characterization of Bio-chemicals and Fresh Biological Tissues in the THz Band ...... 37

3.1 Unique Vibrational Absorption of Bio-chemicals ...... 38

3.2 Hydration Effect on Molecular Compound in THz Band ...... 39

3.3 THz Hydration Sensitivity in Fresh Biological Tissues ...... 42

3.4 THz Spectroscopy and Imaging of Fresh Human Organ Tissues ...... 45

Chapter 4: THz Imaging for Detection and Margin Assessment of Human Malignant

Tumors ...... 49

4.1 Susceptibility of Experimental Setup to THz Spectroscopic Imaging of Hydrated

Tissues ...... 50

4.2 Sample Preparation and Experimental Setup for Formalin-fixed and Paraffin-

embedded (FFPE) Tissue ...... 56

4.3 THz Image and Optical Properties of Malignant Tissues for Endoscopic Sensing

Application ...... 58

4.3.1 Lung Cancer: Neural Endocrine Carcinoma ...... 60 xii

4.3.2 Small Intestine Cancer: Gastrointestinal Stromal Tumor (GIST)...... 62

4.4 Image Processing for Contrast Enhancement...... 65

4.4.1 Image Contrast Enhancement ...... 66

4.4.2 Uniform Background Illumination ...... 69

4.5 THz Polarimetric Sensor for Cancer Margin Identification ...... 72

Chapter 5: THz Macromolecular Response from Abnormal Nervous System: Brain

Tissue exhibiting Alzheimer’s Disease ...... 85

5.1 Background on Alzheimer’s Disease ...... 86

5.2 Sample Preparation and Experimental Setup ...... 88

5.3 THz Spectroscopic Imaging of Human Brain Tissue for Alzheimer’s Detection..

...... 91

5.3.1 THz Imaging of Alzheimer’s and Healthy Control Tissues ...... 92

5.3.2 Origin of Prominent THz Image Contrast between White and Gray Matters

in Alzheimer’s Tissue ...... 95

5.3.3 Case Study of Alzheimer’s Tissues in THz Imaging ...... 98

5.4 Full-EM Simulation of the Simplified Model for White Matter Disorder ...... 102

Chapter 6: Conclusion...... 108

6.1 Summary of this Work ...... 108

6.2 Future Work ...... 110

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6.2.1 Further Considerations of Myelinated Axon Model for EM Simulation .. 111

6.2.2 THz Transparent Metamaterials for Enhanced Spectroscopy and Imaging ...

...... 113

6.2.3 Detection of Macromolecular Characteristic Absorptions in enhanced

Near-field THz Spectroscopy ...... 116

Bibliography ...... 120

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List of Tables

Table 1: Comparison of the THz spectroscopic systems used in this study ...... 33

Table 2: Material properties of lipid content [96] and electrolytes in aqueous Luria-Berani media [97] for the Debye Model ...... 105

List of Figures

Figure 1.1: Electromagnetic spectrum and an example of their applications [3]. The THz band lies between the microwaves and infrared frequencies. THz radiation has been explored in many application areas based on the unique THz properties...... 2

Figure 1.2: (a) Molecular modes and activities in the THz band of the electromagnetic spectrum [7], (b) atmospheric rotational mode absorption in THz band [6], and (c) an example of thermal-emission of D-glucose monohydrate [7] ...... 4

Figure 2.1: Schematic of THz pulse generation through photoconductive emission [24] 15

Figure 2.2: Schematics of the THz-TDS (TPS3000, Teraview Ltd.) in (a) transmission mode and (b) reflection mode...... 17

Figure 2.3: Schematic of THz wave generation through photomixing ...... 18

Figure 2.4: Schematics of the THz-CWS (THOR, Traycer Inc. & OSU) in transmission mode ...... 19

Figure 2.5: Sketch of a frequency multiplier (440 GHz frequency tripler, Vriginia Diodes, Inc.) [42] ...... 21

Figure 2.6: Schematics of a THz frequency extender (transceiver module, Virginia Diodes, Inc.) ...... 22

Figure 2.7: Measured THz signals in transmission mode: (a) detected time domina signals and (b) frequency domain signals through Fast Fourier Transform (FFT) ...... 24

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Figure 2.8: Measured THz signals in reflection mode: (a) illustration of the signal reflections from reference (left) and sample (right) interfaces, showing the parameters in (2.7) and (b) illustration of time gating of the detected time domain signals to remove the initial reflection from the air-substrate interface...... 26

Figure 2.9 (a) simplified schematics of the THz-CWS (THOR, Traycer Inc. & OSU) in transmission mode and (b) detected photocurrent in the THz-CWS ...... 29

Figure 2.10: Examples of the extracted THz data from the absolute value of the detected photocurrent ...... 30

Figure 2.11: Transmittance of (a) a 10% α-lactose monohydrate plus 90% polyethylene pellet with 3.5 mm thickness and (b) a pure α-lactose monohydrate pellet with 0.82 mm thickness (right) ...... 34

Figure 2.12: (a) Frequency selective surfaces (FSS) band-pass filter resonating at 508.5 GHz (Lake Shore Cryotronics, Inc.) and (b) transmittance of the metamaterial filter ..... 35

Figure 2.13: Transmittance of the dried salmon tissues: (a) wild salmons and (b) farm- raised salmons ...... 36

Figure 3.1: Optical properties of α-lactose monohydrate and biotin: (a) refractive index and (b) absorption coefficient ...... 38

Figure 3.2: Optical properties of distilled water, 10% buffered formalin, methanol, and de-ionized water [6]: (a) refractive index and (b) absorption coefficient [50]...... 40

Figure 3.3: Effect of hydration on the THz absorption of α-lactose monohydrate ...... 41

Figure 3.4: Visual (left) and time domain images (right) of a fresh hibiscus leaf measured in reflection mode THz-TDS system ...... 43

Figure 3.5: (a) illustration of reflection mode TDS measurement for the liver tissue from Ovis aries, (b) the thin sliced liver tissue including distilled water, (c) THz reflectivity xvii image, (d) THz refractive index image, and (e) the contrast enhanced THz image based on reflection spectrum...... 43

Figure 3.6: Optical properties of the liver tissue from Ovis aries, bulk water mixed with bound water, and bulk water [50]: (a) refractive index and (b) absorption coefficients . 44

Figure 3.7: Visual images of freshly excised human organ tissues: (a) stomach, (b) heart, and (c) pancreas ...... 46

Figure 3.8: Visual and THz images of freshly excised human pancreas tissue: (a) THz image at 1 THz, (b) THz image at 1.45 THz, and (c) visual image ...... 47

Figure 3.9: Optical properties of fresh stomach, heart, and pancreas tissues: (a) refractive index and (b) absorption coefficients ...... 47

Figure 4.1: (a) visual image of the formalin-fixed liver tissue compared to the THz image at 590 GHz with the descript line (solid black) of cancer margin, (b) the THz image at 590 GHz, and (c) THz reflection spectra for the tumor tissue (L1), the normal tissue (L2), and the blood vessel...... 51

Figure 4.2: Description of the possible two cased of non-uniform tissue contact due to the wedge-shaped tissue with rugged surface ...... 52

Figure 4.3: Description of formalin-fixed liver tissue measurements under different applied pressure; (a) visual images of the liver tissues and (b) THz images at 1.5 THz with outline of the cancerous area respective to different applied pressure ...... 53

Figure 4.4: Extracted THz properties of distilled water compared to the reference [50] for different measurement setups; (a) example measurement setups, (b) refractive indices and (c) absorption coefficients ...... 54

Figure 4.5: Sample preparation and experimental setup: (a) a description of the entire FFPE tissue block processed by a conventional medical tissue slicer, (b) Teraview

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TPS3000 with reflection imaging module (RIM), and (c) schematic diagram for reference and sample measurements...... 57

Figure 4.6: Optical and THz images of the FFPE lung tissue: (a) microphotographs of the tissue stained with hematoxylin and eosin (H&E), (b) the processed THz reflection image at 1.77 THz, (c) 3× magnified image of the red-dotted area for the tumor in the bronchus (airway) with hyaline cartilage plates, and (d) 3× magnified THz image for the corresponding region of (c) ...... 61

Figure 4.7: The mean values of the extracted THz properties of FFPE lung tissues with corresponding standard deviations (vertical bars): (a) refractive indices and (b) absorption coefficients for the regions, L1, L2, and L3, in Fig. 4.6 ...... 62

Figure 4.8: Optical and THz images of the FFPE small intestine tissue: (a) The microphotograph of the H&E stained tissues illustrating the necrotic areas (denoted by “N”), (b) the raw microphotograph of the same tissue, (c) the processed THz reflection image at 1.62 THz, and (d) the processed version of the microphotograph shown in (b) using the same image processing algorithm used for the THz image in (c)...... 63

Figure 4.9: The mean values of the extracted THz properties of FFPE small intestine tissues with corresponding standard deviations (vertical bars): (a) refractive indices and (b) absorption coefficients for the regions, S1, S2, and S3 in Fig. 4.8 ...... 64

Figure 4.10: Optical and THz images of the FFPE small intestine tissue: (a) microphotographs of the tissues stained with hematoxylin and eosin with several nectrotic areas denoted by “N” and (b) THz reflection image at 1.62 THz ...... 66

Figure 4.11: (a) histogram transformation, (b) THz frequency domain image at ~1.6 THz, (c) histogram of the THz frequency domain image, (d) contrast enhanced image (histogram equalization), and (e) histogram of the contrast enhanced image ...... 67

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Figure 4.12: (a) THz frequency domain image at ~1.6 THz, (b) a description of adaptive histogram equalization (AHE), (c) contrast enhanced image by AHE, and (d) contrast enhanced image by CLAHE ...... 68

Figure 4.13: Block diagram of homomorphic filtering ...... 70

Figure 4.14: (a) magnitude of the original THz image in Fourier domain, (b) high-pass filter in Fourier domain, (c) THz image by homomorphic filtering, and (d) contrast enhanced image after homomorpic filtering ...... 71

Figure 4.15: (a) microphotographs of small intestine tissue stained with hematoxylin and eosin with several necrotic areas denoted by “N”, (b) THz image at ~1.62 THz, (c) contrast and background gradient enhanced image (CLAHE & homomorphic filtering), (d) microphotographs of lung tissue stained with hematoxylin and eosin (e) THz image at ~1.77 THz, (f) contrast and background gradient enhanced image (CLAHE & homomorphic filtering) ...... 72

Figure 4.16: Plane wave incidence on the malignant/healthy tissue interface. Diffraction at the material edge rotates the incident E-field polarization creating significant cross- polarization...... 74

Figure 4.17: Plane wave incidence on a planar interface (x-y plane) at an oblique angle 75

Figure 4.18: Decomposed plane wave incidences on a planar interface (x-y plane) at an oblique angle (θi): (a) perpendicular polarization and (b) parallel polarization ...... 76

Figure 4.19: Decomposed plane wave incidences on a planar interface (x-y plane) at an oblique angle (θi): (a) perpendicular polarization and (b) parallel polarization ...... 78

Figure 4.20: Comparison of full wave simulation results with analytic calculations: (a) reflection coefficient of cancerous tissue, (b) reflection coefficient of normal tissue, (c) degree of polarization rotation (DPR) for cancerous and normal tissues, and (d) difference between DPR values of cancerous and normal tissues...... 79 xx

Figure 4.22: Simulated DPR values at 60 º, 62 º, 63 º, 64 º, and 66 º incident angles for plane wave and Gaussian beam illumination ...... 81

Figure 4.23: Simulated DPR values for Gaussian beam illumination as a function of scan position. (a) Healthy tissue to malignant tissue scan and (b) malignant to healthy tissue scan ...... 82

Figure 4.24: (a) components of a polarimetric THz probe, (b) A set of four objective lenses focus the two beams on the tissue, and (c) cross section of the probe ...... 84

Figure 5.1: Description of tissue measurement setups and the corresponding THz images based on raw reflection spectra at 1.29 THz: (a) a schematic of reflection mode THz imaging, (b) the tissue placed on a z-cut quartz window with 2 mm thickness, (b) the tissue placed on metal holding frame, (c) the tissue placed on the calibrated metal holding frame to compensate slanted tissue surface...... 90

Figure 5.2: Optical and THz reflectivity images of the FFPE brain tissue exhibiting Alzheimer’s disease: (a) microphotographs of the tissue stained with hematoxylin and eosin (H&E), (b) the integrated reflection image and (c) THz reflectivity spectra from white and gray matters of the tissues ...... 93

Figure 5.3: The integrated reflection images of (a) AD Case 1, (b) Control 1, and (c) Control 2 and (d) the ratio of reflectivity spectra between gray and white matters of the AD tissues and the controls...... 94

Figure 5.4: Comparison of THz reflectivity spectra between the brain tissues, AD Case 1, Control 1, and Control 2, from hippocampus: (a) gray matter and (b) white matter...... 96

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Figure 5.5: An example of amyloid beta (Aβ42) plaques in the gray matter of Alzheimer’s tissue (AD Case 1) (in-set: magnified image of a β-amyloid plaque, which exhibits neurofibrillary tangles)...... 97

Figure 5.6: The integrated reflection images of (a) AD Case 2, (b) AD Case 3, (c) AD Case 4, and (d) AD Case 5 ...... 99

Figure 5.7: Microphotographs of the AD tissues processed by Luxol fast blue staining: (a) AD Case 2, (b) AD Case 3, (c) AD Case 4, and (d) AD Case 5 (red-dot-circle: hippocampus) ...... 100

Figure 5.8: Pixel intensity distribution of the white matter areas of the AD tissues, which are corresponding to white-circled areas in Fig. 5.7...... 101

Figure 5.9: The ratio of reflectivity spectra between gray and white matters of the AD Cases, Control 1, and Control 2...... 101

Figure 5.10: A description of (a) the bundles of myelinated axons in central nervous system (re-drawn based on the reference [90]) and a simplified unit cell of myelinated axon array for the EM simulation...... 103

Figure 5.11: Comparison of THz reflectivity spectra between the brain tissues, AD Case 1, Control 1, and Control 2, from hippocampus: (a) gray matter and (b) white matter . 106

Figure 6.1: Microscope images of myelinated axons [98]: (a) top-view of myelinated axon in central nervous system (blue arrows: oligodendroscytes and yellow arrows (astroscytes) and (b) cross sectional view of myelinated axons in peripheral nervous system...... 112

Figure 6.2: Geometry of the circular slot FSS unit cell [100] (a) Top view (b) Single Layer Cross-section (c) With superstrate Cross-section ...... 114

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Figure 6.3: Simulated transmission response of the circular slot FSS window and double square loop FSS window for different angle of incidence (θ) [100]: (a) circular slot FSS window (b) circular slot FSS window with superstrate, (c) double square loop FSS window, and (d) circular slot FSS window with superstrate...... 115

Figure 6.4: (a) Visual image of a coin is being compared with its THz images (using TPS300 spectroscopy) on (b) conventional glass slide, (c) z-cut quartz (d) double square loop FSS window as sample holder in grayscale [100] ...... 116

Figure 6.5: Reflectivity of distilled water and liquid human blood exhibiting six levels of HbA1C ...... 117

Figure 6.6: Comparison of various types of optical lenses [114]. (a) conventional lens, (b) near-field superlens, (c) far-field superlens, and (d) hyperlens. Blue and red curves represent propagating waves and evanescent waves, respectively...... 118

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Chapter 1: Introduction

Since the first images were recorded at AT&T Bell Labs by B. B. Hu and M. C.

Huss in 1995, spectroscopic THz-frequency sensing and analysis has been constantly expanding into diverse applications. There is increasing push to bridge the so-called

“THz gap” with the rapid expansion of electronics technologies as well as photonics techniques, such as quantum cascade lasers. Previously, THz waves have been one of the main sensing tools for astrophysicist and have been instrumental in shedding new light onto origins of our universe and the distribution of matter throughout. The goal of this dissertation is to explore the potential of THz-frequency imaging for bio-medical sensing of diseases, conditions and malignancies of human tissues. This introductory chapter starts with a brief discussion of current state of the art in THz science and technology.

Subsequently, we focus specifically on THz bio-medical sensing and discuss the state of the art in THz-band imaging of biological tissues. Finally, we discuss the shortcomings of current methodologies and identify the contributions in this dissertation.

1.1 THz Waves and Their Applications in Spectroscopy and

Imaging

The THz frequency band lies between microwaves and infrared (IR) light and has been called the final frontier in the electromagnetic spectrum. Also termed as the “THz gap” due to the lack of efficient sources and detectors, the large span of this band and its associated properties remained attractive and fruitful areas of research. Since the pioneer work in 1975 on the photoconductive technology controlled by ultrashort laser pulses [1],

[2], much effort has been focused on the development of efficient sources, sensitive detectors, and suitable modulators in THz band. Besides, the advances in nanotechnology and new semiconducting materials in the past decade have brought about compact THz detectors and sources, thus triggering the enormous interest in the THz frequency. In

2 particular, femtosecond-laser-based photoconductive switches integrated with on-chip

THz antennas have enabled new broadband time domain spectroscopy modalities, which can be applied to many remote sensing problems.

THz waves are usually defined as the electromagnetic waves within the frequencies from 0.3 to 10 THz, as shown in Fig. 1.1 [3]. They share unique properties with both microwaves and IR signals. As such, the THz radiation provides several advantages over conventional sensing tools based. In particular, sub-millimeter-scale wavelengths afforded by the THz band enable highly-resolved spectroscopic imaging with less scattering-related corruption issues than IR sensing. In addition, THz waves can penetrate into a wide variety of non-polar materials, thus they can “see” through most common packaging materials such as paper, plastic, clothing, and ceramic, etc. Moreover, similar to IR frequencies, unique spectroscopic signatures of complex chemicals and bio- molecules lie in the THz band due to their intra- and inter-molecular vibrations.

Consequently, highly-specific, non-destructive identification of constituents of samples is possible through THz spectroscopy [4]. Furthermore, rotational absorption spectra of even smaller molecules (such as H2O, O2, CO2, etc.) can be resolved in the THz band [5].

Perhaps, more importantly, THz waves are extremely sensitivity to levels of sample hydration and their non-ionizing nature allows safe assessment of biological samples.

Historically, the primary use of THz radiation was limited to chemistry and astrophysics since the rotational/vibrational spectra of some gases [6] and thermal- emission lines of simple molecules [7] fall into this regime, as demonstrated in Fig. 1.2.

However, recent emergence of commercial THz spectroscopy and imaging systems is

(a)

(b) (c) Figure 1.2: (a) Molecular modes and activities in the THz band of the electromagnetic spectrum [7], (b) atmospheric rotational mode absorption in THz band [6], and (c) an example of thermal-emission of D- glucose monohydrate [7]

fuelling a plethora of new applications in various areas, as shown in Fig. 1.3. Specifically, either very high time- or frequency- based resolution in THz time domain spectroscopy enables to nondestructively analyze the failure location of complex circuitry in microelectronic packages [8], [9]. Also, transparency and high spatial resolution of THz waves have been applied for standoff screening of hidden weapons [10]. Moreover ,the 4 spectral properties of complex molecules in the THz band has been used for quality control and counterfeit screening in the pharmaceutical industry [11].

In addition to the aforementioned application areas, exciting research have been also pioneered by utilizing THz waves, such as food inspection [12], non-destructive evaluation of mural painting [13], and heritage science [14], etc. However, in the past few years, most remarkable expansion of THz technology has seen in medical sciences, spanning diverse applications such as the recent finding below. Thus, in next section, we discuss the contemporary researches and limitation of current THz bio-sensing, as well as the sensitivity discriminative factor of various phases of bio-subjects.

1.2 THz Spectroscopic Imaging in a Bio-medical Tool

Based on the aforementioned capabilities of THz waves, biomedical applications of

THz spectroscopy and imaging haves been extensively studied through in vivo, ex vivo, or in vitro experiments on both animal and human tissues. For example, the margins of skin cancer (basal cell carcinoma) and human breast cancer were successfully assessed in freshly-excised human tissue by THz time domain spectrometer [15], [16]. Also, freshly excised organ tissues from rats were characterized [17] and human nerve tissues were differentiated from other tissue kinds in their optical properties such as refractive index and material loss [18]. Moreover, THz imaging described the time-dependent coagulation process on burned skin, which can be applicable to wound care [19] and the capability of monitoring the curing of dental composites was demonstrated using THz time domain spectroscopy [20].

For the THz biomedical sensing, the main discriminative factors for detecting abnormal tissue depend on the relative phases of the tissue under evaluation. In particular, the high sensitivity of THz waves to the water content within tissues has been identified as one of the key differentiators of in vivo or freshly-excised tissue characterizations. As demonstrated in previous studies [19], [21], the hydration characteristics of biological samples can be accurately quantified using THz waves. In other work [22]-[24], bound water and absorbed-formalin were observed to be the critical factors to differentiate diseased areas from normal regions in the freshly excised and formalin-fixed tissues, respectively.

For clinical medical imaging applications using THz waves, a comprehensive study of freshly excided human tissue groups is highly desirable. Nonetheless, this is often hindered by the lack of availability of fresh tissue and the difficulties related to obtaining the required procedural and government-mandated permissions to process human tissues.

Moreover, bound water confined in the fresh tissues usually vary from sample to sample and the contact pressure within the sample holder (e.g. a quartz substrate) affects the natural distribution of bound water in tissue, thus interfering with an accurate assessment.

Thus, dehydrated tissues, such as paraffin-embedded [25]-[27], dried [28], [29], lyophilized [30], and frozen tissues [31], [32] have often been considered to demonstrate the utility of THz imaging.

In the case dehydrated tissue specimens, such as with biopsies typically prepared for retrospective pathologic study, differences in molecular density and morphological variations due to local and metastatic malignancies can be dominant of the spectroscopic

6 contrast [23], [27]. More importantly, the molecular absorption spectra of tissue constituents can also be detected in the THz image such as the low frequency protein vibrations in [33]. However, it has been reported that de-naturalization (i.e. formalin- fixing and paraffin embedding) as well as hydration of tissue specimens hinder revealing the unique signature-type absorption of their macromolecular constituents in THz band, by broadening the absorption spectra [30], [34]-[36]. Despite these hindrances, certain degree of the absorption level of tissue constituents can be also reflected on the THz image for the dehydrated tissues [37].

As noted above, THz waves have also been recently explored to identify unique spectroscopic responses of complex bio-molecules such as proteins [33], [38]-[40], particularly using the absorption modes of macromolecules lied in THz band. As such, high-specificity, non-destructive identification of constituents of biological specimens has been made possible by THz spectroscopy. For instance, THz waves have been utilized to investigate the vibrational and conformational absorptions of proteins and

DNA. In particular, the binding state (hybridized/denatured or single-stranded/double stranded) of DNA strands was successfully differentiated by utilizing time-resolved THz transmission analysis [38], [39]. DNA hybridization was also quantized using THz spectrometry [39]. In addition, possibility of particular absorption mode of DNA and some proteins were demonstrated using conformational changes [24]. Moreover, low frequency vibrational modes of proteins were recently observed for the first time [33],

[41] using both THz time domain spectroscopy and THz near-field spectroscopy.

The demonstrated sensitivity of THz waves to macromolecular structures (DNA,

RNA or proteins) has brought up possibility of THz wave as more sophisticated diagnostic tool, which can allow histochemical identifications as well as morphological detections of human diseases. However, there are several challenges hindering THz bio- sensing and imaging from being a clinical diagnostic tool such as strong water absorption, high cost, long data acquisition time, and low sensor dynamic range. Among them, the major challenges are strong water absorption and long data acquisition time. In particular, although the high hydration sensitivity of THz wave provides an advantage in certain circumstances, the strong water absorption is still major obstacles in in vivo THz imaging since its low penetration depth to tissues only allow THz signal to carry only surface information of the subjects. In addition, considering clinical/surgical procedures, the data acquisition time needs to be fast enough for in vivo imaging, but current raster-scanned imaging methodology takes much longer time to get THz images. Based on the current limitations, the relevant subjects of in vivo THz sensing would be limited into externally or endoscopically accessible areas such as skin, oral/dental, cornea, respiratory, and gastrointestinal tracts. As such, the THz spectroscopy and imaging might allow faster diagnosis without taking biopsy for further pathological inspection.

Although current THz technologies in bio-medical science is still in immature stage based on above-mentioned limitations, THz sensing has been expanding its utility to more bio-medical application areas, and clinical THz sensing technique (i.e. in vivo) will be possibly available soon as THz technologies continue to improve. In this regard, we

8 have conducted the experimental studies on the fundamentals of THz bio-medical sensing as well as possible utilities of THz imaging as diagnose tools as summarized below.

1.3 Major Contributions of the Dissertation

In this dissertation, a comprehensive study of THz spectroscopy and imaging for bio-medical sensing applications is developed based on the experiments utilizing the state-of-the-art THz spectrometers. Various phases of bio-chemical compounds, human organ tissues, and their associated diseases are investigated either in THz spectroscopy or imaging. Using electromagnetic (EM) theories relevant to the measurement scenarios, transmission, reflection, or optical properties of samples under test is extracted from THz spectroscopy. In the experiments, the particular attention to sample mounting methods for accurate THz measurement is described.

To examine the reliability of the THz time domain spectrometer (TDS) used in this study, the TDS spectral data from organic samples are validated through comparison with other types of spectroscopic systems. Specificity and hydration sensitivity of THz wave are also demonstrated by bio-molecules and biological tissue studies. On the basis of the verifications, several fresh human organ tissues are characterized in optical properties in

THz band. The results show that human organ tissues can be differentiated primarily in their absorption at certain range of THz frequencies.

In addition to the quasi-homogeneous fresh human tissues which exhibit only healthy regions, liver tissue samples including metastatic tumor are imaged by the THz-

TDS system. For the new endoscopic sensing application, the THz imaging is also

9 performed on cancerous lung and small intestine tissues, which can be endoscopically assessable. The cancer tissue studies demonstrate clear image contrast of cancer boundary as well as smaller morphological variations due to their different absorption characteristics. Going a step forward based on the results, a polarimetric sensing scheme fitted into the endoscopic sensor is introduced as a possible method to enhance the sensitivity for cancer margin identification. EM simulation is conducted to investigate the performance of the polarimetric sensing scheme. The simulation result implies that the significant enhancement of the sensitivity for cancer margin detection might be attainable through THz polarimetry.

Motivated by the possible causes of Alzheimer’s disease such as amyloid beta plaques and demyelination, THz spectroscopic imaging is also performed on human brain tissues to investigate available THz responses for the detection of Alzheimer’s disease. In this study, the clear contrast between gray and white matters is consistently monitored on the THz images of Alzheimer’s tissues, thus hypothesizing this finding for THz

Alzheimer’s detection. Further case studies are performed in THz imaging and the results still support the hypothesis. To speculate the origin of the clear contrast, the reflection spectroscopy is compared both in gray and white matters, and the electromagnetic simulation is perform on the simplified model of myelinated axon (main component of white matter). Based on this, we present the correlation of myelin sheath thickness with

THz spectroscopic responses of white matter in Alzheimer’s tissues. These results show that clear contrast might be originated primarily from THz response of white matter, possibly due to demyelination.

1.4 Dissertation Overview

In Chapter 2, we review the three major THz signal generation and detection schemes adopting photoconductive antennas, photomixers, and Schottky diode-based frequency multipliers chains. Subsequently, we dilate on the post-processing methods to calculate transmission, reflection, and material properties based on raw data obtained

THz spectrometers. We also compare state-of-the-art THz spectroscopic systems in term of cost, frequency span, resolution, dynamic range, and data acquisition time. Moreover, experimental validation is presented through the comparison study of spectroscopic data measured by three THz systems with the different generation/detection schemes, establishing the performance baselines for the commercial THz-TDS systems used throughout this work.

After clearly quantifying the THz sensing performance of our TDS system and establishing effective data processing mechanisms, we next focus on experimental study to demonstrate accuracy, specificity, and hydration sensitivity of THz wave particularly applicable to bio-medical sensing. In Chapter 3, we present spectroscopic signatures of bio-chemicals based on their molecular vibration in THz band, and we discuss the hydration effect on the molecular compound. High hydration sensitivity of THz wave is also demonstrated through THz imaging of biological tissues. Subsequently, we present

THz imaging and spectra of freshly-excised human organ tissues, resulting in distinguishable material properties between the tissues.

In Chapter 4, we focus on THz imaging for detection and margin assessment of human malignant tumors based on different THz responses from abnormal tissue areas due to their morphological and constitutional variations. . We first illustrate the interfering effects of experimental setup on THz cancer margin identification by utilizing liver tissues with malignant tumor adjacent to normal tissue. We outline clear procedures to prepare formalin-fixed and paraffin-embedded tissue specimen for reflection-mode imaging. Next, we present THz images and optical properties of lung and small intestine cancer specimens to demonstrate the potential of THz imaging particularly in endoscopic sensing application, which allows preventive medical care. This chapter also covers the image processing algorithms to enhance the contrast of malignancies in THz image.

Furthermore, we introduce the THz polarimetric sensor as a viable concept to significantly improve the sensitivity to tumor boundary as well as nerve bundles in tissue imaging.

In Chapter 5, we discuss the THz detection of Alzheimer’s disease, for the first time.

Alzheimer’s is a nervous system disorder originated from mutations in proteins as well as morphological variations, which are expected to exhibit discriminative spectroscopic behavior. Thus, we discuss the background of Alzheimer’s disease including leading hypotheses of its root cause, and our experimental procedure is briefly introduced. Finally, we present the THz spectroscopy and imaging of brain tissues exhibiting Alzheimer’s disease and their controls. Based on the experimental data, we establish a hypothesis for the THz detection of Alzheimer’s disease, and the hypothesis is supported by electromagnetic wave simulation of the simplified model for myelinated axon in white

12 matter. Finally, Chapter 6 summarizes the finding of this work and suggests future research directions.

Chapter 2: Fundamentals of THz Spectroscopy

In this chapter, we review the physical fundamentals related to the generation and detection techniques in the THz range, as well as the comparison of the state-of-the-art

THz spectroscopic systems. We limit our discussion to three established THz generation- detection schemes adopted for time domain spectroscopy (TDS), continuous wave spectroscopy (CWS), and vector network analyzer (VNA) with frequency multipliers, which are commercially available. In particular, we investigate photoconductive antenna, photo-mixing, and GaAs Schottky diode frequency multiplication technologies. The post- processing methods to extract optical properties from raw data are also developed. In addition, the performances of the three systems are discussed for application suitability and the cross-validation of their spectroscopy is presented trough experimental comparison.

2.1 Generation and Detection Schemes of THz Signal

2.1.1 Photoconductive Antenna

Perhaps the most established of THz generation techniques is the photoconductive emission achieved through optical excitation by an ultrafast, pulsed near-infrared laser, as shown in the inset of Fig. 2.1 [24]. This method inherently results in the emission of broadband THz pulses, which covers a frequency range typically from 0.06 THz to 3

THz. In particular, THz photoconductive emitter relies on the production of near IR

(NIR) pulses using a femto-second laser to excite THz pulses through a biased emitter antenna, which consists of two planar metal electrodes on a low temperature grown gallium arsenide (LT-GaAs) substrate. The antenna is designed to exhibit radiation

Figure 2.1: Schematic of THz pulse generation through photoconductive emission [24]

15 bandwidth in the desired THz frequencies. The pulsed THz emission from photoconductive antenna is produced when the current density, j, of a biased semiconductor is modulated on sub-pico-second time-scale of the pulsed laser source, viz. . The change in the current density (photocurrent) arises from two processes. On the emitter side, the rapid change of the carrier density via femto-second laser illumination leads to the THz emission. Particularly, by the illumination of the

Ti:Sapphire laser (800nm with energy 1.55 eV), the electrons (and holes) are excited into the conduction band over the band gap of the low temperature (LT)-GaAs (~869nm with energy 1.43 eV). The photo-generated electron-hole pairs excited near the LT-GaAs surface are free to move. A bias applied via the metal electrodes accelerates the free electrons and holes in opposite directions, leading to a rapid change in the current density. The movement of charge results in a changing dipole, which produces a THz transient in the antenna, which is then radiated into free space through typically a high- resistivity hyper-hemispherical silicon lens. On the other hand, the detector operates in reverse to the emitters. The external THz electric field accelerates the photo-excited carriers without the external bias, and the generated photocurrent is utilized to gauge the incident THz electric field. Finally, by varying the optical path length to the detector, the detected photocurrent is sampled, thus both the amplitude and phase of the incident THz wave can be obtained.

THz-TDS system is operated either in transmission or reflection geometry, as illustrated in Fig. 2.2. The incident terahertz wave radiated from the emitter is either transmitted through the sample or reflected by the sample before detection. Thus, the

(a)

(b)

Figure 2.2: Schematics of the THz-TDS (TPS3000, Teraview Ltd.) in (a) transmission mode and (b) reflection mode.

17 spectroscopic signatures and optical properties of the sample can be obtained using either geometry. Due to the current setup of the THz-TDS system at the OSU HELIOS Lab, our experimental studies are carried out in both geometries by utilizing a nitrogen-purged chamber for transmission mode and two computer controlled x-y micro-translation stages for refection mode.

2.1.2 Photomixing

One of the alternative approaches to THz generation is photomixing technology that is optical heterodyne conversion, achieved by two continuous wave (CW) lasers with identical polarization. To be specific, the two lasers with frequency ω1 and ω2, typically in near infrared, are spatially overlapped in optical fiber and modulated at the difference frequency. Such heterodynes are fiber-coupled into the photoconductor (i.e. LT-GaAs or

Figure 2.3: Schematic of THz wave generation through photomixing

Figure 2.4: Schematics of the THz-CWS (THOR, Traycer Inc. & OSU) in transmission mode

LT-InGaAs) in THz emitter, as described in Fig. 2.3. Owing to the finite photo-created carrier lifetime in between biased metal rods, the semiconductor is only able to respond to the slower difference frequency (ωTHz = ω2-ω1). As a result, the THz signal is radiated via a hyper-hemispherical silicon lens by broadband antenna integrated within the photo- mixer.

Fig. 2.4 describes the THz-CWS system (THOR, Traycer Inc. & OSU) that we used in our study. The main components of this system are a current and temperature control module, a pair of distributed feedback (DFB) laser diodes, LT-InGaAs photo-mixers for both emitter and detector, gold coated parabolic mirrors, and a micro-translated sample holding stage. The tunable DFB laser diodes operate around at 1539.9 and 1544.6 nm, respectively. The laser frequencies are tuned by both current (~0.6 GHz/mA) and

19 temperature (~14 GHz/K in the temperature range between 276.15 K and 321.15 K) controls. As such, the CW system is capable of collecting THz frequency spectrum between about 60 GHz and 1000 GHz with a minimum frequency step of 10 MHz. In this system, the difference (THz) of the signal is radiated via a hyper-hemispherical silicon lens by broadband bow-tie antenna integrated within the photo-mixer. THz wave is collimated and focused into the sample position, and the transmitted THz wave is collimated and focused onto the detector. Finally, the detected THz signal is superimposed with the heterodyne signal, and is fed into a lock-in amplifier. To alleviate noise signal level, the lock-in integration time can be set from 21 to 650 ms.

2.1.3 Schottky Diode Frequency Multiplier Chain

Schottky diode frequency multiplier chains have been recently used for THz generation. As mentioned earlier, the previous THz generation technologies demand

Ti:sapphire and IR lasers to generate ultrashort (femto second) pulses and heterodynes coupled into photoconductive materials, respectively. However, the GaAs Schottky diode chains enable frequency multiplication reachable to THz regime by conventional electronic source, which is conventional microwave to mmWave oscillator. For example, as seen in Fig. 2.5, the frequency tripler comprises of a metal waveguide housing split in the E-plane, a GaAs Schottky varactor diode chip, and two microstrip embedding circuits on quartz substrates [42]. In brief, the fundamental excitation (ω0) transfers from the WR-

6 rectangular waveguide to the microstrip circuit via an E-Plane probe, and the signal propagates through the ω0 pass filter to the VDI varactor diode. The second (2ω0) and the

Figure 2.5: Sketch of a frequency multiplier (440 GHz frequency tripler, Vriginia Diodes, Inc.) [42]

third (3ω0) harmonics are generated by the varator diode and transmitted through the microstrip lines. The output waveguide (WR-2) topologies are designed to cut-off the propagation of fundamental (ω0) and the second harmonic (2ω0) signals, thus only the third harmonic (3ω0) signal is coupled to the output waveguide. As such, higher multiplication factor for THz generation can be accomplished by cascading doubler and tripler stages, which is called the frequency multiplier chain.

The frequency multiplier chains are employed by the frequency extender module

(Virginia Diodes, Inc.) connected into conventional vector network analyzer (VNA-VDI), as described in Fig. 2.6. In particular, as RF and LO inputs from VNA at fRF and fLO (i.e.

9-14 GHz) are fed into the extender module, the frequencies of the signals are multiplied by the factor of M or N (i.e. M = N = 54) after each amplification stage. The multiplied

RF input at fRF×M (i.e. 500-750 GHz) is spitted for THz transmission and down-

Figure 2.6: Schematics of a THz frequency extender (transceiver module, Virginia Diodes, Inc.)

conversion with the multiplied LO input at fRF×M (i.e. 500-750 GHz). On the other hand, the detected THz signal at fSIGNAL is transferred into a mixer to be down-converted.

Finally, the output signals at “fLO×N fRF×M” and “fLO×N fSIGNAL” are placed in IF frequency band (i.e. 20 MHz - 9 GHz), thus the conventional VNA can process the detected THz signal.

2.2 Post-processing of Raw THz Spectroscopy

It is well-known that the refractive index (i.e. dielectric permittivity) and the absorption coefficient (i.e. material losses) can be determined using the electromagnetic wave transmission and reflection coefficients of the sample under test if the sample’s thickness and layered composition are known. Unfortunately, with modern THz

22 spectrometers except for the vector network analyzer with frequency extenders, one cannot measure the reflection and transmission coefficients concurrently. In addition, the extremely small wavelengths of the THz band introduce non-repeatable errors when the transmission and reflection coefficients are measured on separate experimental stations.

Moreover, the effects of the sample holder must also be considered for an accurate characterization. For instance, biological tissue samples are typically placed on low-loss z-cut quartz substrates. In this setup, the multiple reflections and Fabry-Perot transmission resonances introduced by the substrate must be factored out for an accurate extraction of the tissue’s inherent characteristics. This is typically achieved using a reference measurement (with the sample holder placed in the setup without the actual sample), which is regarded as a “calibration” step. Considering the calibration, we summarize the post-processing methods to extract optical properties as well as transmission and reflection coefficients from raw THz spectroscopy produced by our

TDS and CW systems. The post-processing for VNA-VDI system is aside from this summary since this system is capable to provide both transmission and reflection coefficient directly and the extraction method is analogous to the case of TDS system.

2.2.1 Time Domain Spectrometer

As noted above, to calculate the refractive index and absorption coefficient of a sample, the measurement of the sample’s response must be accompanied by a reference measurement to calibrate the effect of the sample holder. In addition, as the time domain

THz spectroscopy uses a pulsed transmit/receive setup, the data collected either in

(a) (b) Figure 2.7: Measured THz signals in transmission mode: (a) detected time domain signals and (b) frequency domain signals through Fast Fourier Transform (FFT)

transmission or reflection mode measurement needs to be processed using the Fast

Fourier Transform (FFT).

In transmission mode, the detected pulses (in time domain) are converted into the frequency domain, as illustrated in Fig. 2.7. If we denote the electric field strengths as

Er(ω) and Es(ω) for reference and sample signal, respectively, the ratio can be written as

 i ( n s 1) d E s   i   Ae  T ( )e c (2.1) E r   where A and ∆φ are magnitude and phase of the field ratio from measured reference and sample pulses. In (2.1), s(ω) denotes the frequency dependent complex refractive index of the sample such that s(ω) = ns(ω) + ks(ω), where ns(ω) and ks(ω) are the refractive index and the extinction coefficient, respectively. Also, T(ω) is the Fresnel transmission

24 coefficient and d is the thickness of sample. The absorption coefficient αs can be calculated using

2 k s  s  (2.2) c where ω is angular frequency and c is the speed of light. Equation (2.1) can also be re- written by explicitly defining loss and refractive index contributions to the transmission phase, viz.

   i s d i ( n 1) d E   i  s s  Ae  T ( )e 2 e c (2.3) E r  

From (2.3), the refractive index of sample can be readily obtained using the phase delay introduced by the sample (referenced to the calibration measurement Er(ω))

c n s  1    (2.4)  d

Using the calculated refractive index of sample, ns from (2.4), the Fresnel transmission coefficient, T(ω), can be evaluated for the normal incidence, and finally the absorption coefficient, αs, is calculated as:

2  A     ln   s   (2.5) d  T   

Particularly for paraffin-embedded tissue samples or certain rigid organic/biological samples, since the THz wave is directly transmitted through the samples, the expression in (2.3) can be applied with

4 n n T    a s 2 (2.6) n a  n s 

25 where na is the refractive index of air.

In reflection mode, a reference measurement is also required similar to transmission mode THz spectroscopy. In addition, when a sample holder (e.g. z-cut quartz) is used, two dominant reflection pulses can exist in the detected signal, which is from air-quartz and quartz-air/quartz-sample interfaces, as shown in Fig. 2.8 (a). Among them, the first reflection from the air-quartz interface can be removed to alleviate complex calculation procefures for multi-layer sample topologies either by pre- and post-processing methods:

First, the optical delay stage can be adjusted to isolate the reflected pulse from the top

(a)

(b)

26 surface of the quartz window before the measurements. The other is to apply time gating method as a post processing step, as illustrated in Fig. 2.8 (b). As such, THz pulses reflected directly from the sample interface can be measured or extracted. Through a simple FFT, the reference and sample electric field responses Er(ω) and Es(ω) are calculated. The ratio of the two is the reflection coefficient, r which is expressed as

E n q cos  q  n s cos  s n q cos  q  n a cos  a r  s   (2.7) E n cos   n cos  r n q cos  q  n s cos  s q q a a

Here, the incident angle of the THz wave entering the z-cut quartz window is θq, and the refracted angles in the air and sample boundaries are θa and θs, respectively. In (2.7), na, ns, and θa are known parameters. θq and θs can be also evaluated by Snell’s law, viz.

n a sin  a  n q sin  q  n s sin  s (2.8)

Equation (2.7) can be rearranged in terms of the parameters of the sample as

2 2 (1  r )  n q cos  q  (1  r ) n a cos  a  n q cos  q n s cos  s  (2.9) 1  r   n a cos  a  1  r   n q cos  q 

2 in which, cos  q  1  n a sin  a n q  . Moreover, since real n s cos  s   n s cos  s , the refraction angle for the sample, θs, can be extracted using (2.8) and (2.9) as

 real n s cos      a cot  s  s   (2.10)  n a sin  a 

Finally, using the complex index of refraction, the real refractive index and absorption coefficient of the sample are obtained using

n s  real n s  (2.11)

2  imag n s   s  (2.12) c

2.2.2 Continuous Wave Spectrometer

Based on the coherent detection principle, the THz signal in the CW system is estimated through the detected photo current, Iph, which is an interference pattern, as shown in Fig. 2.9. The detected photocurrent is determined by the amplitude of the THz electric field, ETHz, and phase difference, ∆φ, between the THz wave and the IR laser signal [43]:

2 f I ph  E THz cos(   )  E THz cos (  L eff ) (2.13) c where f denotes the THz frequency, c is the speed of light, and the effective optical path difference is

 L eff  n eff  L  n eff L A  L B  L C (2.14)

where neff is the effective refractive index for overall system path length in the fixed delay line system. As described in Fig. 2.9, LA and LB are the optical paths from the laser source to the emitter and from the laser source to the detector, respectively. LC represents the

THz path from the emitter to the detector. From Eq. (2.13) and (2.14), the actual THz

(a)

(b)

Figure 2.9 (a) simplified schematics of the THz-CWS (THOR, Traycer Inc. & OSU) in transmission mode and (b) detected photocurrent in the THz-CWS

data can be obtained by extracting the envelop function, ETHz, as shown in Fig. 2.10 (a).

Thus, the transmittance can be determined by

sam  E  T ( w )  20 log  THz  (2.15) ref  E   THz 

(a) (b) Figure 2.10: Examples of the extracted THz data from the absolute value of the detected photocurrent

sam ref E where E THz and THz denote the extracted envelopes of the sample and the reference data, which are the extrema of the interference patterns. Here, we note that the extrema of sample data are positioned at different frequencies from that of the reference data, as illustrated in Fig. 2.10 (b). Therefore, to compare the envelopes of both data, the extrema of the sample data need to be interpolated on equally spaced frequency grid of the extrema of the reference data.

Traditionally, the way to extract the phase information of the THz signal is to use a mechanical delay stage or an optical phase modulator to vary ∆L in Eq. (2.13). However, the variable delay lines result in considerable data acquisition time to scan full range of

THz spectrum (i.e. 0.1 THz - 1.2 THz) since the measurements at several delay stage positions per each frequency step are necessary to trace out the waveform. For example, considering 100 MHz, 300 ms, and 10 points for the frequency step, the integration time, and the number of delay stage position, respectively, the expected data acquisition time is

30 more than 18 hours for both reference and sample measurements.

Alternatively, the relative phase difference can be also evaluated in the fixed delay line system by the comparison between the detected THz waveforms for sample and reference [43]. In particular, as addressed in Eq. (2.13), the frequency and the optical path difference determine the oscillation period of the detected photocurrent, which refers to the fringing space. Here, for the reference waveform, THz wave travels through air with the constant refractive index (nair ≈ 1), thus the fringing space is nearly constant independent of frequencies. As such, the position of extrema can be defined as:

ref mc f  , m  1,2 ,3 , extrema ,m ref (2.16) 2  L eff

sam In the case of the sample waveform, the effective optical path difference, E THz , can be changed with the frequency dependent refractive index, nsam, and the thickness, d, of the sample. Thus, compared to the reference signal, the extrema of the sample signal are not equally spaced in frequency, and the fringing pattern is shifted, as depicted in Fig.

2.10 (b). Based on this fact, through comparing the extrema of the sample and the reference data, the relative phase difference can be achieved, thus the refractive index of the sample can be calculated using the equation [43],

ref  f  sam ref  extrema ,m  ref n  n d   L   L    1  L sam air eff eff sam eff  f  (2.17)  extrema ,m 

However, in practical data analysis, there are several major difficulties to obtain relevant refractive indices. In particular, the detected photocurrent is not perfectly 31 sinusoidal, and the constant fringing space between the extrema is unattainable from the actual reference data. More importantly, the relative order, m, of the extrema for the reference and the sample data can be ambiguous.

2.3 Comparison of State-of-the-art THz Spectroscopic Systems

The aforementioned THz generation and detection techniques are well-established, and the all types of spectroscopic system are commercially available from, particularly for the systems used in this study, Teraview, Ltd., Toptica Photonics, AG., Agilent

Technologies, Inc., and Virginia Diodes, Inc.. As such, in general, they can be applicable to material characterization, non-destructive evaluation, and imaging, etc.; however, in practical, their application area can be limited based the inherent system performances such as spectral range, dynamic range, frequency resolution, and data acquisition times, as shown in Table 1.

In particular, for the broadband spectroscopy and imaging, the TDS is most desirable since it adopts time domain signal generation technique based on femto-second lasers source. As such, though simple Fourier transform of measured pulses, one can obtain 0.6 to 3THz spectroscopy in very short time. However, to understand highly resolved physical behaviors in certain frequency ranges such as in condensed matter physics, the CWS can be more efficient due to its high spectral resolution capability while this system requires too much data acquisition time to take broad band spectral image. VNA-VDI can be utilized for multiples purpose due to high dynamic range, short data acquisition times, and its flexible setup compared to the TDS and the CWS

Table 1: Comparison of the THz spectroscopic systems used in this study

Vector Network THz Time Domain Continuous Wave Analyzer (Agilent Spectrometer Spectrometer Technologies, Inc.) with (Teraview, Ltd) (Toptica Photonics, AG.) frequency extenders (Virginia Diodes, Inc.)

signal Schottky Diode generation Photoconductive antenna Photomixing Frequency Multiplier technique Chain

0.09 ~ 0.14 THz 0.14 ~ 0.22 THz spectral 0.06 ~ 3 THz 0.06 ~ 1 THz 0.22 ~ 0.33 THz range 0.325 ~ 0.5 THz 0.5 ~ 0.75 THz

39.6 dB at 0.15 THz 50 dB at 0.1 THz dynamic 43.1 dB at 0.9 THz 80 ~ 100 dB 30 dB at 0.6 THz range 42.1 dB at 1.5 THz over all spectral ranges 10 dB at 1 THz 38.2 dB at 2.5 THz

spectral varied by VNA 7.5 GHz sub-gigahertz resolution specification

data a few seconds for the A few hours for the full sub-second for the each acquisition full spectral range spectral range spectral range time

required many sensitive optical alignments. However, the discrete frequency extender modules with the higher frequency limit up to 0.75 THz is irrelevant to broadband spectroscopy measurement. Thus, considering the fitness to our study, we primarily utilize the TDS system to investigate biological and human tissues in broadband spectroscopy and imaging.

2.4 Validation of THz Spectroscopy

Despite the increasing number of available state-of-the-art THz spectrometers, contemporary studies have rarely presented the validation of THz spectroscopy by experimental methods. In this regard, it is valuable to compare the spectroscopic data of various types of samples yielded through different THz generation/detection mechanisms such as the TDS, the CWS, and VNA-VDI systems. In particular, we investigate transmission spectroscopy of solid-state samples, biological samples, and a metamaterial device in room-temperature and without nitrogen-purging.

The α-lactose monohydrate is a standard reference sample for THz spectrometer due to its well-known strong and narrow absorption signature at 530 GHz [44]. Thus, we prepared two lactose samples to demonstrate the sensitivity as well as the specificity to chemicals of the THz spectrometers. One is a 10% α-lactose monohydrate plus 90%

(a) (b) Figure 2.11: Transmittance of (a) a 10% α-lactose monohydrate plus 90% polyethylene pellet with 3.5 mm thickness and (b) a pure α-lactose monohydrate pellet with 0.82 mm thickness (right)

34 polyethylene pellet, which is a transparent material to the THz band. The other is a pure

α-lactose monohydrate pellet. As exhibited in Fig. 2.11, the all transmission measurements for both samples exhibit the strong absorption signature around at 530

GHz. In particular, the center frequencies of the absorption were detected at 529.7 GHz,

535.5 GHz, and 530 GHz by TDS, CWS, and VNA-VDI, respectively. This shows that

TDS and VNA-VDI results are more close agreement with the expected frequency [44].

The transmittance spectra also demonstrate high sensitivity in magnitude to the dose of the lactose compounds even considering the different thickness of the pellets.

Artificially designed metamaterials such as Frequency Selective Surfaces (FSS) can be one of appropriate gadget to reveal the accuracy of THz spectrometers. Here, we measured a cross-dipole FSS band-pass filter resonating at 508.5 GHz, as presented in

Fig. 2.12. The spectra from the TDS and CWS demonstrate the exact expected resonant frequency and also demonstrate excellent agreement up to 800 GHz.

(a) (b) Figure 2.12: (a) Frequency selective surfaces (FSS) band-pass filter resonating at 508.5 GHz (Lake Shore Cryotronics, Inc.) and (b) transmittance of the metamaterial filter

(a) (b) Figure 2.13: Transmittance of the dried salmon tissues: (a) wild salmons and (b) farm-raised salmons

Finally, we present the validity of our spectrometer systems for biological tissue characterization. Based on the availability, we tested wild and farm-raised salmon tissues to investigate the possible contrast originating from their lipid contents and chemical compositions [45], [46]. For these experiments, eight specimens were taken from both wild and farm-raised salmons, and the results were averaged. We also note that the salmon tissues were air-dried in between two glass slides for eliminating highly absorptive water content in THz band to maximize measurable frequency range. As depicted in Fig. 2.13, even though the scattering effect due to the surface roughness of the tissue at the measurement positions may result in a slight difference of transmission, the overall spectra exhibit excellent agreement between CWS and VNA-VDI systems. More importantly, the THz spectra demonstrate the potential to distinguish wild salmons from farm-raised salmons including higher lipid content, which is more transparent in THz band.

Chapter 3: Spectroscopic Characterization of Bio- chemicals and Fresh Biological Tissues in the THz Band

In this chapter, several bio-chemicals and fresh biological tissues are characterized in spectroscopic signature, hydration sensitivity, and distinguishable optical properties by the THz-TDS system. To demonstrate THz specificity and spectroscopic signature, we start to investigate several liquid contents and bio-molecules particularly utilized for biological tissues. We consider two organic molecules, namely α-lactose monohydrate and biotin, both of which have strong absorption peaks in the THz band. Following the solid-state compounds, we investigate the optical properties of distilled water, 10% buffered formalin, and methanol to establish a baseline measurement for the subsequent characterization of biological tissues. Such procedure constitutes not only a validation of the property extraction method, but also illustrates the resolution and specificity of the

THz-TDS equipment used for this measurement. Subsequently, high hydration sensitivity of THz wave is presented the through THz imaging of a fresh hibiscus leaf and a fresh

Ovis aries (domestic sheep). In the end, the freshly excised tissues from major human 37 organs are scrutinized in THz spectroscopic imaging and optical properties for the development of the comprehensive human tissue database in THz regime.

3.1 Unique Vibrational Absorption of Bio-chemicals

Unique spectroscopic behaviors of complex chemicals either in natural or synthetic form including drugs and explosives lie in the THz band [47], allowing for highly-specific, non-destructive identification of constituents of samples under test.

Among them, α-lactose monohydrate and biotin are investigated for THz spectroscopic signature due to low frequency vibrations in condensed phase. In particular, since the absorption characteristics (i.e. resonant frequencies) of these samples have been well studied and are also theoretically known [44], the resolution and specificity of the THz-TDS can be readily evaluated. The measurements for both samples in the form of solid tablet are performed in transmission mode TDS at room temperature. Nitrogen

2 60 -lactose monohydrate

1] biotin 1.8 - 50

40 1.6 30 1.4

20 refractive refractive index

1.2 -lactose monohydrate 10 biotin absorption coefficient [cm 1 0 0.5 1 1.5 2 2.5 0.5 1 1.5 2 2.5 frequency [THz] frequency [THz] (a) (b) Figure 3.1: Optical properties of α-lactose monohydrate and biotin: (a) refractive index and (b) absorption coefficient 38 gas is purged into the measurement chamber to minimize the effect of atmospheric water vapor absorption. As shown in Fig. 3.1, the absorption for α-lactose monohydrate is most prominent at 0.53 THz, 1.2 THz, and 1.37 THz, which are well matched to the results by

Roggenbuck’s group [43]. This constitutes a clear validation of the efficacy of our THz-

TDS. For the biotin compound, the absorption signatures are marked around at 0.54 THz,

1 THz, 1.34 THz, 1.54 THz, 1.78 THz, 2.05 THz and 2.3 THz, almost identical to

Korter’s experiments at room temperature [24]. We remark that THz-TDS reveals the potential to determine the clear differentiation of bio-molecules with absorption lines as previously demonstrated by THz CW spectrometers [43], [48], [49]. It also needs to note that the extracted refractive index and absorption coefficient slightly diverge from the results that were published in the literature. However, such differences are typical due to the different amount or thickness of samples, local humidity, and more specifically temperature variations.

3.2 Hydration Effect on Molecular Compound in THz Band

As is well-known, THz waves interact strongly with water. Considering most biological tissue samples include substantial amount of water, understanding of the properties of water in the THz regime is fundamental to studying the interaction of THz radiation with biological tissues. For this, distilled water droplets were placed on a z-cut quartz window and were measured in reflection mode TDS. To verify the accuracy of our

THz spectrometers and obtain base-line data, THz properties were extracted from raw data by the post-processing procedure utilizing Fresnel equations. In addition, formalin

5 700

distilled water ] distilled water 1

- 600 10% buffered formalin 4 10% buffered formalin 500 methanol methanol deionized water (reference) 400 deionized water (reference) 3

300 refractive refractive index 2 200

100 absorption coefficient, [cm

1 0 0 0.5 1 1.5 2 0 0.5 1 1.5 2 frequency [THz] frequency [THz] (a) (b) Figure 3.2: Optical properties of distilled water, 10% buffered formalin, methanol, and de-ionized water [6]: (a) refractive index and (b) absorption coefficient [50].

and methanol fixing is typically employed in histopathological examination. As such, we also investigated the optical properties of methanol and 10% buffered formalin, which has been traditionally used for tissue diagnosis in medical research.

Figure 3.2 shows the extracted properties compared with the optical properties of de-ionized water from the reference [50]. The properties of the distilled water are well matched to the reference data for de-ionized water. However a few absorption lines are monitored at the clustered frequencies occurring relatively strong water vapor absorption such as ~ 1.1THz and ~ 1.7THz. These absorption lines are expected because nitrogen gas was not purged to eliminate the water vapor effect. The properties of 10% buffered formalin exhibit slightly lower values than distilled water, however, all features are almost exactly the same as those of de-ionized water. Such close agreement is not surprising, considering the fact that the 10% formalin is diluted by de-ionized water

no water 1 drop 5 drops

10 -lactose monohydrate 9 + 1 water drop + 2 water drops 8 + 3 water drops + 4 water drops 7 + 5 water drops + 6 water drops 6 + 7 water drops + 8 water drops 5

4 absorbance[a.u.] 3

2 1.19 THz

1 530 GHz 1.37 THz

0 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 frequency [THz]

Figure 3.3: Effect of hydration on the THz absorption of α-lactose monohydrate

(1:10). In contrast, the methanol exhibits much lower values in refractive index and absorption coefficient.

As noted above, the THz response to fresh tissues can be characterized on the basis of two factors: (1) the interaction between tissue and bound water (biological water) molecules, as well as (2) their specific molecular constituents (i.e. proteins such as myoglobin [34], hemoglobin [41], and lysozyme [33]). In this regard, the THz response can be quite unique for major human tissue groups, thus utilizing for the reference to determine biological specificity. However, the resonant absorption due to molecular vibrations is typically masked by much stronger water absorption [51]. For example, bio- molecules within a liquid matrix exhibits reduction in sharp absorption peaks due to line

41 broadening effect such as the study of hydrated α-lactose monohydrate compound, as shown in Fig. 3.3. In particular, as hydration level (the number of water droplet) increases, the sharp absorption lines at higher frequencies (1.19 THz and 1.37 THz) become either broader or disappeared, even though the low frequency vibrational mode absorption is still dominant due to its incomplete dissolution. As a result, the overall absorption of the tissues increases almost linearly with frequency in most case (see Fig.

3.2).

Nonetheless, such high hydration sensitivity of THz wave can also enable the characterization and sensing of fresh biological tissues and associated malignancies through the degree of absorption. Thus, based on these validations of the accuracy of

TDS data, the extraction algorithm, and the hydration effect to absorption characteristics, we next focus on hydration sensitivity on biological tissues and optical properties of major human organ tissues.

3.3 THz Hydration Sensitivity in Fresh Biological Tissues

As seen above, water content is highly absorptive in the THz regime. Therefore, the hydrated materials can exhibit good contrast to the surrounding materials in THz imaging

[18, 19]. In order to demonstrate the hydration sensitivity creating the contrast of THz images, we prepared two fresh biological tissues such as a hibiscus leaf and a liver tissue from Ovis aries, and they are measured in reflection mode TDS. Fig. 3.4 exhibits the high resolution image of a fresh hibiscus leaf with good sensitivity to the water

Figure 3.4: Visual (left) and time domain images (right) of a fresh hibiscus leaf measured in reflection mode THz-TDS system

content. Specifically, higher water concentration is shown along with orange and yellow

lines, corresponding to the leaf vein transporting the biological fluid.

We next tested the liver tissue from Ovis aries due to commercial off-the-shelf

availability. An assumption before this experiment was that bound water (or biological

water) can be differentiated from bulk water (or distilled water) by the high hydration

liver tissue from Ovis aries

Glass bound water + bulk water 1 mm Tissue spacer z-cut quartz air bubble

z-cut quartz window

(a) (b) 15 15 5 10 10 4 4 5 5 3 3

0 0 position position 2 -5 -5 2 -10 -10 1 THz 0.72 THz 1 0.72 THz1 1 THz 0.72 THz 1 THz -15 frequency = 0.71452-15 THzfrequency = 1.0062 THz (c) (d) (e) -10 0 10-10 0 10 Figureposition 3.5: (a) illustrationposition of reflection mode TDS measurement for the liver tissue from Ovis aries, (b) the thin sliced liver tissue including distilled water, (c) THz reflectivity image, (d) THz refractive index image, and (e) the contrast enhanced THz image based on reflection spectrum 43

4 400 liver tissue from Ovis aries liver tissue from Ovis aries 3.5 bulk water mixed with bound water bulk water mixed with bound water bulk water 300 bulk water 3

2.5 200

2 refractive refractive index 100

1.5 absorption coefficient [cm-1]

1 0 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 frequency [THz] frequency [THz] (a) (b) Figure 3.6: Optical properties of the liver tissue from Ovis aries, bulk water mixed with bound water, and bulk water [50]: (a) refractive index and (b) absorption coefficients

sensitivity of THz waves. For the test, the liver tissue was sliced as thin as possible and was placed on the z-cut quartz window for reflection mode TDS measurement. A small area inside the tissue slice was intentionally dissected and filled with distilled water. To avoid the hydration evaporation, a glass window was also placed on the top of the tissue with about 1mm thickness dielectric spacer, as shown in Fig. 3.5(a). During this preparation, an air bubble was also accidentally created at the area between the quartz and the glass windows, as depicted in Fig. 3.5(b). As we applied a moderate pressure to the slide to ensure slice uniformity, biological water from the tissue seeped into the dissected area and mixed with distilled water.

As demonstrated in Fig. 3.5(c) and (d), it is hard to clearly differentiate the dissected area both in reflectivity and refractive index image except for the air bubble area.

However, after applying simple image processing to enhance the contrast, much better differentiation was easily obtained, as illustrated in Fig. 3.5(e). For this, we can clarify

44 the origin of the image contrast through the calculated optical property for each region. In particular, the absorption property for each region are primarily accounted for the contrast of the image based on the reflection spectra, while refractive index for each region rarely contribute to the image contrast, as demonstrated in Fig. 3.6. Considering the bound water is the main factor of the absorption characteristics at the liver tissue area, we can conclude that the subtle difference between the bound water and the bulk water mixed with bound water can be detectable through the high hydration sensitivity of THz wave.

3.4 THz Spectroscopy and Imaging of Fresh Human Organ

Tissues

With the approval and the implementation of OSU Internal Review Board (IRB) certification, we started processing human tissue samples using our THz-TDS system for the 0.06-3THz band. In particular, based on tissue availability, time-domain spectroscopy measurements were conducted on three tissue specimens including stomach, heart, and pancreas, as shown in Fig. 3.7. All tissue specimens were harvested from the same subject in the autopsy at the Ohio State University Medical Center (OSUMC) and measured within five hours after the time of death of the individual. Each specimen was placed on a separate quartz window and was characterized individually. For the relatively small specimen sizes (about 10 mm × 10 mm), each THz imaging scan took less than 1 hour with 200 µm scan step size. While each specimen was undergoing measurement,

(a) (b) (c) Figure 3.7: Visual images of freshly excised human organ tissues: (a) stomach, (b) heart, and (c) pancreas

other tissues remained refrigerated and concealed in order to avoid further dehydration and decomposition.

The stomach and heart specimens were normal tissues with no local or metastatic disease. Even though dark brown area was appeared in the stomach specimen, it was relatively distant to the tissue surface. As such, the observed THz images displayed fairly uniform contrast across most of the samples, indicating the homogeneity of the tissue samples. However, the pancreas specimen appeared on gross examination to contain a large amount of fat and was not typical of a normal postmortem pancreas. The lighter color components are largely lipid (fat), and the darker areas containing pancreatic islets, stromal/connective tissues, arteries and veins. Microscopically, the pancreas appeared again abnormal, with disproportionally large areas of fat. The darker colored highly cellular areas demonstrate necrosis (un-programmed/sudden cell death) in addition to fibrosis. Areas containing viable pancreatic tissue (close to normal appearing) are present but are generally small and scattered throughout larger areas demonstrating the

pancreas (II)

1.1 0.4 1 20 20 0.35 0.9

0.8 0.3 15 15 0.7 0.25 0.6

10 10 0.5 0.2

dimension[mm] dimension[mm] 0.4 0.15 0.3 5 1 THz 5 1.45 THz 0.2 pancreas0.1 (I) 0.1 0 (a) 0 (b) (c) 0 5 10 15 20 0 5 10 15 20 Figure 3.8: Visualdimension and [mm] THz images of freshly exciseddimension human [mm] pancreas tissue: (a) THz image at 1 THz, (b) THz image at 1.45 THz, and (c) visual image

aforementioned pathologic changes. Typical postmortem changes are observed as well.

As a result, significant contrast was obtained for the THz response of the pancreas tissue,

indicative of morphological and histochemical variations within the sample. The THz

images are shown in Fig. 5 and clearly resolve two distinct regions aforementioned as the

lighter and darker areas.

stomach stomach

3 250 1] heart - heart pancreas (I) 200 pancreas (I) pancreas (II) pancreas (II) 2.5 150

100 refractive index 2

50 absorption coefficient [cm

1.5 0 0.2 0.4 0.6 0.8 1 1.2 0.2 0.4 0.6 0.8 1 1.2 frequency [THz] frequency [THz] (a) (b) Figure 3.9: Optical properties of fresh stomach, heart, and pancreas tissues: (a) refractive index and (b) absorption coefficients

The optical properties for the three tissue groups were also calculated, as demonstrated in Fig. 3.9. Beyond the margin of error, this result reveals clear difference in refractive index in the frequency band from 0.35 THz to 0.95 THz. The difference in absorption coefficient is also shown at the frequency band from 0.06 THz to 0.65 THz.

Two different optical properties were observed for the pancreas tissue over the whole frequency range. As expected, the lighter color area exhibit lower absorption characteristics compared to the darker area because relatively low loss fat content.

Similar to the result for this specimen, it is expected that abnormal tissue areas exhibit different THz response, thus we investigate malignant tumor adjacent to normal tissue for human cancer assessments in the following chapter.

Chapter 4: THz Imaging for Detection and Margin Assessment of Human Malignant Tumors

We present THz images of human organ tissues with associated malignancies and metastatic diseases to develop a comprehensive study of THz imaging for cancer assessments using THz-TDS system. The discriminative THz responses to cancerous vs. normal tissue regions are expected due to the differences in hydration level, morphology and histochemistry. First of all, to discuss the susceptibility of the tissue mounting setup to THz imaging, two formalin-fixed human liver tissues including tumor area adjacent to normal tissues are examined in different measurement setup. Subsequently, human lung and small intestine tissues including metastatic diseases are investigated to demonstrate the utility of THz imaging toward endoscopic sensing application. In all cases, it is demonstrated that discriminatory information can be readily obtained from THz images of TDR signals. In addition, we discuss the image processing algorithms to enhance the contrast of the obtained raw THz images. Furthermore, we propose a new THz

49 polarimetry to significantly improve the sensitivity to tumor boundaries in tissue imaging, and its performance is evaluated through full EM wave simulation.

4.1 Susceptibility of Experimental Setup to THz Spectroscopic

Imaging of Hydrated Tissues

As aforementioned, it is most desirable to perform in vivo or fresh tissue measurements for clinical medical imaging; however, the studying of the freshly excised tissue is often hindered by the lack of availability of raw tissue and equipment for tissue preparation. Therefore, here, we utilize formalin-fixed tissues for our initial cancer study in THz band. In the formalin-fixed tissue specimen, the absorbed-formalin mixture

(typically 10% formalin buffered by 90% demonized water) was observed to be critical factors to differentiate diseased area from normal area [52]. However, the buffered formalin can be dehydrated during raster-scanned imaging process, which typically takes about an hour for 1cm × 1cm tissue specimen. Moreover, the hydrated and flexible specimens usually have no evenly flat surface due to the lack of the relevant slicing tools.

As a result, such dehydration and non-uniform surface can cause the significant distortion of the obtained THz image. Thus, much sophisticated experimental setup is required to alleviate this problem.

In this regard, here, we investigate the susceptibility of tissue mounting setup to

THz imaging for detection and margin assessment of human malignant tumors. Based on the availability at the time of the autopsy, a liver tissue including malignant tumor were

(a)

(b) (c) Figure 4.1: (a) visual image of the formalin-fixed liver tissue compared to the THz image at 590 GHz with the descript line (solid black) of cancer margin, (b) the THz image at 590 GHz, and (c) THz reflection spectra for the tumor tissue (L1), the normal tissue (L2), and the blood vessel.

first tested to get THz image. At the autopsy, the tissue was kept in formalin for over 48 hours to assure that the tissues completely fixed. The liver tissue specimen exhibits clear difference between normal (brown) and cancerous (white) area in visual image, as shown in Fig. 4.1(a). After time domain reflection-mode THz response for the sample was recorded, three different regions (normal, cancerous, and blood vessel areas) were investigated based on their respective spectroscopic responses. As illustrated in Fig.

4.1(c), the best image contrast was observed around at 590 GHz due to the strong reflection from a blood vessel area in the sample. However, the overall image contrast is relatively weak to assess the precise cancer margin.

Figure 4.2: Description of the possible two cased of non-uniform tissue contact due to the wedge- shaped tissue with rugged surface

We believe that the weak image contrast is primarily due to the following two factors: First, the tissue sample had non-uniform contact with the quartz window due to thick and wedge-shaped tissue topology with a rather rugged surface, as illustrated in Fig.

4.2. As such, the non-uniform contact with the quartz tissue holder results in air-gaps between the tissue and the holder. These regions show up as bright white areas in the

THz images as in Fig. 4.1(a) (right). More importantly, this tissue specimen had been exposed to ambient air in its transit (over several hours), thus the formalin had almost completely evaporated from the tissue by the time of the measurement. As mentioned earlier, this effect can be critical because the formalin content is regarded as one of the key contrast factors in addition to water content [52]. Nevertheless, despite the low contrast, the raw THz image at 590 GHz clearly displays the outline of the cancerous area

(ripple-like-curvature), as shown in Fig. 4.1 (a) and (b).

A second test was performed to further investigate the effect of the non-uniform tissue contact with the fixture. To do so, we selected a liver tissue specimen that had

(a)

(b) Figure 4.3: Description of formalin-fixed liver tissue measurements under different applied pressure; (a) (a) visual images of the liver tissues and (b) THz images at 1.5 THz with outline of the cancerous area respective to different applied pressure

fairly flat interface, however the tissue was fairly thick (1 ~ 1.5mm) and wedge-shaped.

The sample specimen includes the same type of metastatic liver malignancy as the previous sample, as shown in Fig. 4.3. Before our experiments, this tissue was kept in a formalin jar, and it was measured under three different applied pressures, as described in

Fig. 4.3(b). The first measurement was implemented through the same procedure as the

(a)

(b) (c) Figure 4.4: Extracted THz properties of distilled water compared to the reference [50] for different measurement setups; (a) example measurement setups, (b) refractive indices and (c) absorption coefficients previous experiment. For the second measurement, heavy metal plates were utilized to affix the sample onto the quartz window so that the pressure on the tissue sample can be increased to assure the uniform contact between the quartz windows. Finally, additional weight was placed on the sample to increase the pressure and ensure good contact.

Despite the use of the heavy-weight metal loads, some imperfect contact areas were still observed in the THz image, as shown in Fig. 4.3(b). Nevertheless, we do confirm that additional pressure can mitigate the uniform contact problem, allowing for better contrast in the THz-TDS images.

Unlike THz imaging which is rather qualitative based on the contrast between different tissue characteristics, extraction of THz properties (i.e. refractive index and

54 absorption) is much more sensitive to the experimental set-up for reference and sample signals. Therefore, in the scenario above, the pressure difference between the reference and sample measurements typically results in significant variation of the signal time-of- flight between the emitter and the quartz window. This difference is detrimental to accurate extraction of THz properties. An example is shown in Fig. 4.4, where the errors in the reference measurement are shown to result in significant differences in the extracted refractive index and absorption coefficient of the distilled water (dashed- curves). Precise reference measurement, on the other hand, can very accurately reproduce the actual response of the liquid water in the THz band.

Based on the experiments, the following rules can be applied to the sample preparation of freshly-excised and formalin-fixed tissues: i) The tissue sample must be prepared as uniformly flat as possible, ii) a perfect tissue contact with the quartz window must be assured by utilizing a custom sample holding apparatus, which allows for precise control of the applied pressure, and iii) utmost care must be exercised during the experiment and the THz properties should be carefully monitored using a reference setup that is as close to the sample measurement as possible.

Alternatively, dehydrated tissue specimens can be also considered to detour the issues mentioned above. Thus, next, we present the preparation method of formalin-fixed and paraffin-embedded tissue specimen for THz image.

4.2 Sample Preparation and Experimental Setup for Formalin-

fixed and Paraffin-embedded (FFPE) Tissue

As discussed in the Introduction, the differentiable THz spectra of local and metastatic malignancies can be also detected in the dehydrated tissue specimens, such as histopathological sections typically prepared for retrospective pathologic study.

Therefore, given the availability of the tissue specimens, the rest of our studies deploy the formalin-fixed and paraffin-embedded (FFPE) tissue specimens, which are prepared by standard FFPE fixation and embedding protocol for histologic diagnosis [53]. Through this, we can also avoid the possible susceptibility originated from the abovementioned experimental setup.

For the biopsies, medical tissue slicers (microtomes) are utilized to prepare thin tissue slices for subsequent pathologic assessment. However, available thicknesses are typically about 10µm, which is too thin for THz reflection spectroscopy. Specifically, when the tissue being tested is substantially thinner than what the measurement bandwidth can resolve, the internal structure of the tissue cannot be resolved [54]. As a result, special measurement setups must be used [26], [55] and moreover, excessively thin tissues are not appropriate for reflection-mode THz imaging.

Thus, the entire tissue blocks were processed by stripping several slices of the tissue surface using a conventional medical tissue slicer to minimize surface roughness and ensure flatness, as described in Fig 4.5(a). We remark that through this process, the actual tissue surface was exposed directly through the paraffin layer for THz reflectivity

(a) (b)

(c) Figure 4.5: Sample preparation and experimental setup: (a) a description of the entire FFPE tissue block processed by a conventional medical tissue slicer, (b) Teraview TPS3000 with reflection imaging module (RIM), and (c) schematic diagram for reference and sample measurements.

measurements. The adjacent slices created by the slicer were kept for pathological inspection through hematoxylin and eosin (H&E) staining.

In the following experiments, a commercially-available time domain spectroscopy

(TDS) system (TPS3000, Teraview, Ltd) equipped with reflection imaging module (RIM) is utilized, as shown in Fig. 4.5(b). The tissue block was mounted on a 2 mm thick z-cut quartz window and a metal weight was placed on the top of the tissue block to assure good contact between the tissue and the quartz window. The THz beam focused onto the

57 top surface of the quartz window had a parallel polarization, an incident angle of 30º, and a diffraction limited spot size of ~360 µm at 1 THz, as depicted in Fig 4.5(c). The mounted samples were raster-scanned by 100 µm and 150 µm resolution for the lung and the small intestine tissues, respectively. The scanning was performed at room temperature.

4.3 THz Image and Optical Properties of Malignant Tissues

for Endoscopic Sensing Application

Many research groups have conducted experiments on both animal and human tissues through in vivo, ex vivo or in vitro measurements either in transmission or reflection mode sensing. Most notably, some of the new THz applications are now transitioning into active clinical research, such as in vivo cancer margin assessment for skin cancer using hand-held THz probes [56], [57] as well as corneal hydration monitoring [58]. Beyond these non-invasive applications of THz biomedical imaging, some researchers have also proceeded to expose the target site using surgical procedures.

To complement such efforts, we present in this section the utility of THz imaging for various malignancies that are noninvasively reachable via a thin endoscope.

Malignancies along the gastrointestinal (GI) and respiratory tracts are readily accessible through endoscopy and tissue biopsies are commonly performed in order to retrieve samples from the suspected disease site. With the availability of miniaturized components, it is now possible to integrate THz transceivers into small form-factor endoscope housings, allowing for in-situ spectroscopy of the suspicious site. It is thus 58 crucial to quantify the utility of THz imaging for human tissues that can be readily reached with an endoscope.

To this end, small intestine tissue and human lung tissues, including bronchus regions are considered. For the current study, we utilize formalin-fixed and paraffin- embedded (FFPE) samples with their associated histopathology for a direct comparative study. As shown, compositional (e.g. fibrous or protein content) and morphological variations in these tissues lead to discriminatory spectroscopic information for malignant areas. The THz images presented in this study are the first experimental results for these organs. Although FFPE lung tissue was previously investigated [55], the tissue preparation was quite unconventional since the authors used a 10µm-thick tissue slice soaked in bulk water. Also, the malignancy in this earlier work was squamous cell carcinoma and the sample did not include the bronchus region.

For both of the FFPE tissue measurements, no spectroscopic response was associated with the cancer type in the form of unique absorption peaks in the reflection spectra. In general, THz images for both tissues show better contrast and finer resolution at higher frequencies due to the relatively constant value of the refractive index of paraffin and the embedded tissues. The raw 2D images based on reflection spectra exhibited non-uniform background response originating from imperfect/slanted contact between the quartz substrate and the tissue sample. To remove this effect and improve image contrast, two image processing techniques were applied. The first approach uses homomorphic filtering [59] and second uses contrast limited adaptive histogram equalization (CLAHE) [60]. More details of the processing techniques are addressed in

59 the following Section 4.4. In the 2D image, 20 random points were selected to calculate the refractive index and absorption coefficient of the region of interest and the mean and standard derivation were calculated and tabulated.

4.3.1 Lung Cancer: Neural Endocrine Carcinoma

The optical image of the H&E stained FFPE lung tissue and the corresponding THz reflectivity image measured at 1.77GHz are shown in Fig. 4.6. Pathologically, the tissue exhibits well differentiated neural endocrine carcinoma cells spread completely through the sample. While no necrosis or apoptosis was noted, a dense fibrous capsule is observed surrounding the margin of the tumor and the carcinoid. A visual comparison of the measured THz image given in Fig. 4.6(b) reveals that all morphological features of the tissue is clearly differentiated at this representative frequency. In particular, dense fibrous capsules are well defined with their higher reflectivity indicated by the brighter color in the THz image. More importantly, the tumor in the bronchus (airway) with hyaline cartilage plates and fibrous tissue stained in pink color shown in Fig. 4.6(c) is pronounced in the THz image in Fig 4.6(d). The THz image also identifies the diagonal feature across the tissue for the region L4 in Fig. 4.6(a), which is indicating the primitive boundary of lung tissue cells. In Fig. 3, we present the extracted electrical properties for the 0.06 - 1.8

THz band. It is interesting to note that although the fibrous capsule area (L2) exhibited almost identical refractive index as the malignant area (L1) up to 1.1 THz, its absorption coefficient is clearly much more pronounced. This result is also consistent with the previous studies that used excised breast cancer samples [15]. Overall, the mean values of

(a) (b)

the refractive indices of all three lung regions were measured to vary between n=1.6 (at

300GHz) and n=1.3 (at 1.6THz), with all three tissue regions exhibiting very similar behavior (within the standard deviation of the ensemble) as seen in Fig. 4.7(a). On the contrary, as shown in Fig. 4.7(b) the associated absorption coefficients were observed to differ significantly, well beyond the standard deviation margins. The cartilage area exhibits the most absorption (~300cm-1 at 1.6THz) and the fibrous capsule area exhibit the lowest absorption (~150cm-1 at 1.6THz). Thus, the THz response of the respective

(a) (b) Figure 4.7: The mean values of the extracted THz properties of FFPE lung tissues with corresponding standard deviations (vertical bars): (a) refractive indices and (b) absorption coefficients for the regions, L1, L2, and L3, in Fig. 4.6

tissue regions with varying morphologies are largely attributed to their respective absorption characteristics, which in turn are mainly due to the differences in the structural and molecular densities for each type of tissue.

4.3.2 Small Intestine Cancer: Gastrointestinal Stromal Tumor (GIST)

The malignancy of small intestine tissue we studied was classified into a gastrointestinal stromal tumor (GIST) diffusely involving the intestine wall with scattered necrotic parts, as identified in Fig. 4.8(a). No primary tumor was reported on this specimen and the cancer is spread throughout the sample.

One common result of cancer is the presence of areas of necrosis (cell death) within the diseased tissue. This is certainly seen in this sample with the tumor necrosis areas in the H&E stained image being denoted by “N”. It is important to note that the usual process of identifying necrotic areas in these optical microscopic images is via direct visual inspection by experienced pathologists using a specific staining process. The 62

(a) (b)

original microphotograph of the H&E stained tissue is also shown in Fig. 4.8(b). As seen, it is quite difficult to intuitively classify all necrosis areas based on this optical image without further magnification.

Nonetheless, as seen in Fig. 4.8(c), the THz reflectivity of the FFPE tissue sample readily captures the region of tumor necrosis. Such high level of differentiation is primarily based on the contrast in the absorption behavior of the respective GIST areas,

63 as shown in Fig. 3(b). As observed, the darker areas in the THz image correspond to the necrosis areas on the H&E stain image shown in Fig. 4.8(a). As clearly demonstrated by this example, the effectiveness of the THz reflectivity image in differentiating necrotic areas can be used as an alternative to or in conjunction with the standard tools that are based on manual examination of stained tissue samples.

The extracted electrical properties of the tissue were also evaluated for representative regions as illustrated in Fig. 4.9. In particular, the GIST region is denoted by S3, the necrosis area by S2, and the reference paraffin area by S1, respectively. As expected, the refractive index of the paraffin control region was measured to be 1.5, with negligible absorption compared to the other two regions. Diffuse cancerous areas (S1) and nectrotic areas (S2) have again been observed to exhibit well-differentiated absorption coefficients, as seen in Fig 4.9(b), once again illustrating the accuracy and utility of reflection mode THz imaging for cancer margin assessment.

(a) (b) Figure 4.9: The mean values of the extracted THz properties of FFPE small intestine tissues with corresponding standard deviations (vertical bars): (a) refractive indices and (b) absorption coefficients for the regions, S1, S2, and S3 in Fig. 4.8

Overall, the morphological details on these tissue samples can be readily identified in the THz reflection images, which correlate extremely-well with the histopathological sections. Refractive indices and absorption data were also extracted at different regions in the specimen, which provided additional data to discriminate between normal and pathologic tissues. This study clearly demonstrates that the absorption characteristics of the respective tissue regions contribute most to the image contrast in the THz band for lung and small intestine tissues. Although this initial study is based on dehydrated FFPE tissue samples, it provides a strong demonstration of the potential of THz imaging for the endoscopic assessment of accessible organs.

4.4 Image Processing for Contrast Enhancement

In the previous section, the image processed THz image clearly demonstrates all morphological features and tumor boundary in the lung tissue as well as the localization for the majority of the GIST necrosis in the small intestine tissue. In the raw THz images, however, a background gradient is also observed due to non-uniform contact between the material of the sample holder (z-cut quartz window) and the tissue surface. For instance, the raw THz image of the small intestine tissues exhibits darker background in the lower right hand corner and a lighter background in the upper left hand corner, as demonstrated in Fig. 4.10(b). This inconsistency limits the automatic differentiation of tumor, necrosis and healthy tissue regions. Thus, here, we develop a simple image processing algorithm to identify the high contrast regions in the THz image in addition to the boundary of the tissue sample.

(a) (b) Figure 4.10: Optical and THz images of the FFPE small intestine tissue: (a) microphotographs of the tissues stained with hematoxylin and eosin with several nectrotic areas denoted by “N” and (b) THz reflection image at 1.62 THz

4.4.1 Image Contrast Enhancement

One of the typical ways to process image is to utilize histogram of image, which is a plot of the probability of an occurrence of a certain pixel level (intensity) in the image. In particular, histogram equalization is used to increase/enhance contrast of an image.

Generally, contrast is the difference between maximum and minimum pixel intensity.

Thus, the distribution of image histogram needs to spread out to enhance image contrast.

To do this effectively, a transformation using cumulative distribution function is performed to redistribute the intensity value from the pixels of original image (vi,j) to the pixel of new image (hi,j) [61] such as

 cdf v i , j   cdf min  h i , j v i , j   round   # of grey levels   1 (4.1) # of pixels  cdf    min 

(a)

110 200 100 90 80 150 70 60 100

50 pixel of # # of pixel of # 40 50 30 20

10 0 normalized pixel intensity

20 40 60 80 100 120 0 0.2 0.4 0.6 0.8 1 # of pixel normalized pixel intensity (b) (c)

110 100 200 90

80 150 70 60 100

50 pixel of # # of pixel of # 40 30 50 20

10 0 normalized pixel intensity

20 40 60 80 100 120 0 0.2 0.4 0.6 0.8 1 # of pixel normalized pixel intensity (d) (e) Figure 4.11: (a) histogram transformation, (b) THz frequency domain image at ~1.6 THz, (c) histogram of the THz frequency domain image, (d) contrast enhanced image (histogram equalization), and (e) histogram of the contrast enhanced image

110 100 90 80 70 60

50 # of pixel of # 40 30 20 10

20 40 60 80 100 120 # of pixel (a) (b)

110 110 100 100 90 90 80 80 70 70 60 60 50

# of pixel of # 50 # of pixel of # 40 40 30 30 20 20 10 10

20 40 60 80 100 120 20 40 60 80 100 120 # of pixel # of pixel (c) (d) Figure 4.12: (a) THz frequency domain image at ~1.6 THz, (b) a description of adaptive histogram equalization (AHE), (c) contrast enhanced image by AHE, and (d) contrast enhanced image by CLAHE

where vi,j is an intensity at the pixel coordinate (i, j). Also, cdf and cdfmin denote a cumulative distribution function (CDF) and the minimum non-zero value of the CDF. As a result, histogram equalization can accomplish a higher contrast, as shown in Fig. 4.11.

This algorithm usually enhances the global contrast rather than improving the local contrast of an image to bring out more detail. In particular, the enhancement by histogram equalization can be achieved well only when the distribution of pixel intensity is similar over the entire image (i.e. uniform bright or dark backgrounds). For the image containing 68 significantly lighter or darker area, the contrast in those regions will not be sufficiently enhanced, as shown in Fig. 4.11(e). To mitigate this problem, adaptive histogram equalization (AHE) can be utilized [60], [62], [63]. As seen in Fig. 4.12(b), this algorithm performs the transformation for each pixel based on CDF derived from its neighborhood distribution while ordinary histogram equalization uses the transformation function based on entire pixels of an image. Therefore, AHE can realize better local contrast of an image. However, AHE tends to over-amplify noise in relatively homogeneous regions of an image, as demonstrated in Fig. 4.12(c). This issue can be resolved by limiting the contrast that is allowed through histogram equalization, as shown in Fig. 4.12(d). This contrast limiting approach is referred as contrast limited adaptive histogram equalization

(CLAHE).

4.4.2 Uniform Background Illumination

The simplest model for image formation in image processing is the illumination- reflectance model [64]. Based on this model, the intensity at any pixel is the product of the illumination of the planar scene and the reflectance of the objects in the scene such that

I x , y   L  x , y  R  x , y  (4.2) where I is the image, L is scene illumination, and R is scene reflectance. Reflectance (R) is resulted from the properties of objects while illumination (L) is caused by the lighting conditions. Here, to alleviate the non-uniform illumination, the illumination (L) needs to be removed. Typically, compared to the reflectance from object, the illumination varies

High-pass I (x,y) ln exp I (x,y) Filter

Figure 4.13: Block diagram of homomorphic filtering

slowly across the image. Based on this, the illumination can be regarded as low frequency component in frequency domain, thus by applying high-pass filter, the reflectance component can be extracted without non-uniform illumination, as shown in Fig. 4.13. For this method, a logarithm is usually taken because this multiplicative model can be an additive model so that the two components can be separated linearly in frequency domain

(referred as homomorphic filtering [59]) such that

ln I  x , y   ln L  x , y   ln R  x , y  (4.3)

Figure 4.14 above shows the processed image using homomorphic filtering. In particular, after performing Fourier transform of the logarithmic image intensities, the frequency domain image is filtered by a simple Gaussian high-pass filter with σ = 8.

Compared to the original THz image, the processed image exhibits relatively uniform background illumination, and image contrast is also enhanced.

Based on the aforementioned algorithms, the THz images of FFPE lung and small intestine tissues are processed and compared with the microscopic tissue images and THz images from raw measurement data, as shown in Fig. 4.15. For both tissues, non-uniform background illumination (gradient background) almost disappeared on the processed

(a) (b) frequency domain image frequency domain image

110 110 100 100 90 90 80 80 70 70 60 60

50 50

# of pixel of # # of pixel of # 40 40 30 30 20 20 10 10

20 40 60 80 100 120 20 40 60 80 100 120 # of pixel # of pixel (c) (d) Figure 4.14: (a) magnitude of the original THz image in Fourier domain, (b) high-pass filter in Fourier domain, (c) THz image by homomorphic filtering, and (d) contrast enhanced image after homomorpic filtering

images. As seen in Fig. 4.15(a) and 4.15(c), the processed THz image of the small intestine tissue resolves the localization of the major tumor necrosis over all three tissue parts (top-right, bottom-left, and bottom-right) as several black or dark-gray spots. For the lung tissue, in this raw image (see Fig. 4.15(e)), a dark area for region L1 exhibits some ambiguity of the border line of fibrous capsule and it also masks morphological information in the region. On the other hand, the processed image, as shown in Fig.

4.15(f) clearly resolves the fibrous capsule adjacent to the dark area (L1) of the fibrous capsule and it also reveals much more details about the dark-masked region.

# of pixel 100 120 140 160 20 40 60 80

20 40 frequencydomainimage 60 # # of pixel 80 100

120

# of pixel of # # of pixel of #

140 120 100 80 60 40 20

140 120 100 80 60 40

20 140

0.2

20 20

(a) (b) 0.3 (c)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

40 40 0.4

60 0.5 60

# # of pixel

0.6 80 80

0.7 100 100

0.8

120 120

0.9

140 140

(d) (e) image (f)domain frequency

4.5 THz Polarimetric Sensor for Cancer Margin Identification

Terahertz detection of malignant tissue has so far been based on the different optical properties between cancerous and healthy tissue. Typically, malignant tissue exhibits higher values of refractive index and absorbance than normal tissue. However, when tissue hydration is included, the refractive index of cancerous regions of biological tissues has been measured to be very close to those of healthy tissues, with typically less than 5% difference. As such, previous studies in THz imaging focused on tissue 72 absorption characteristics where differences in the order of 20-30% have been documented. These close values of index and absorption make it difficult to identify the tumor margins using THz imaging. In addition, previous methods relied on THz reflectometry using only a single polarization of the THz emitter.

Considering such difficulty, we propose a novel THz polarimetry to improve the sensitivity to tumor boundaries in tissue imaging. In general, polarimetry determines the cross-polarization effect in both magnitude and phase due to complex sample structures

(i.e. birefringent crystals) or inhomogeneous materials. Obviously, all materials are microscopically inhomogeneous; however, when the properties of a material vary on much smaller scale compared to wavelength, we can consider the material to be macroscopically homogeneous. As such, the most human tissues can be regarded as homogenous materials in THz band since typical cell size of human tissues is much less than 100 µm except for certain muscle cell and oocyte [65]. In addition, the penetration depth is less than 400 µm for fresh human tissues [66], which include strongly absorptive water content, thus the backscatter is negligible. As a result, we can assume that only inhomogeneity is expected at tumor boundaries.

Specifically, edge diffraction for reflected wave from the malignant/healthy tissue interface would significantly enhance THz image contrast and margin identification, when the cross-polarized E-field component of the reflected signal is observed. As an example, a typical reflection-mode polarimetric sensing scenario is illustrated in Fig. 4.16, assuming an incident plane wave on the malignant/healthy tissue interface with an oblique polarization. Due to the discontinuity in refractive index and absorption

Figure 4.16: Plane wave incidence on the malignant/healthy tissue interface. Diffraction at the material edge rotates the incident E-field polarization creating significant cross-polarization.

coefficient, strong edge diffraction occurs when the wave impinges on the tissue boundary. This diffraction rotates the incident E-field polarization creating significant cross-polarization, thus the ratio of the polarization magnitude on the tissue boundary is much higher compared to the homogeneous tissue areas. As a result, the degree of polarization rotation can be clear indicative of cancer margins.

To demonstrate the performance of the THz polarimetry, we first investigate the degree of polarization rotation (DPR) through both analytical reflection coefficient expressions and full-wave simulations for TE and TM plane wave incidences. Based on the calculation and simulation results, we identify the suitable incidence angle to maximize the detection of the tumor boundary based on the extracted optical properties from healthy and cancerous sections in the formalin-fixed liver cancer. Following that,

z E i φ

k i θ ϵ , µ 1 1 y x ϵ2 , µ2

Figure 4.17: Plane wave incidence on a planar interface (x-y plane) at an oblique angle

the full-wave simulation is modified to have a Gaussian Beam incidence, and the DPR value is evaluated as a function of displacement from the boundary.

General expression of the electric field for the linearly polarized plane wave incident on a planar interface (x-y plane) at an oblique angle, as shown in Fig. 4.17., can be written as

i  jk  y sin   z cos   1 (4.4) E  E 0  xˆ sin   yˆ cos  sin   zˆ cos  sin   e

where k1 is a wave number in the region 1. To investigate reflection coefficient at oblique incident angle for the linearly polarized plane wave, it is convenient to decompose the electric field into its perpendicular and parallel components relative to the plane of incidence (x-y plane), as illustrated in Fig. 4.18.

The decomposed incident electric fields can be expressed as

i  jk  y sin   z cos   1 i i (4.5) E perp  xˆ E 0 sin  e

z z i E paral i r r E perp E perp H paral

i H paral θ θ r θ θ r i i i r k r i i r k r H perp k H perp k E paral ϵ , µ ϵ , µ 1 1 y 1 1 y x ϵ2 , µ2 x ϵ2 , µ2

θ θ t t t E t E paral perp t t H paral H perp k t k t (a) (b) Figure 4.18: Decomposed plane wave incidences on a planar interface (x-y plane) at an oblique angle (θi): (a) perpendicular polarization and (b) parallel polarization

i  jk 1  y sin  i  z cos  i  (4.6) E paral  E 0  yˆ cos  sin  i  zˆ cos  sin  i  e

Similarly the reflected fields can be written with respective reflection coefficients as

r  jk  y sin   z cos   1 r r (4.7) E perp  xˆ  perp E 0 sin  e

r  jk 1  y sin  r  z cos  r  (4.8) E paral   paral E 0  yˆ cos  cos  r  zˆ cos  sin  r  e

Also, using the transmission coefficients, the transmitted fields can be expressed as

t  jk 2  y sin  t  z cos  t  E perp  xˆ T perp E 0 sin  e (4.9)

t  jk 2  y sin  t  z cos  t  (4.10) E paral  T paral E 0  yˆ cos  cos  t  zˆ cos  sin  t  e

From the given electric fields, the corresponding magnetic fields can be calculated. By applying the boundary conditions on the continuity of the tangential components of the electric and magnetic fields, we can obtain the reflection and the transmission coefficients and the relation between the incident, reflected and refracted angles (Snell’s law) such as [67]

 2  2 cos  i   1  1 cos  t  perp  (4.11)  2  2 cos  i   1  1 cos  t

2  2  2 cos  i T perp  (4.12)  2  2 cos  i   1  1 cos  t

  1  1 cos  i   2  2 cos  t  paral  (4.13)  1  1 cos  i   2  2 cos  t

2  2  2 cos  i T paral  (4.14)  1  1 cos  i   2  2 cos  t

sin  i  sin  r  sin  t (4.15)

Defining the DPR as a ratio between reflected powers of perpendicular and parallel incident waves, the ratio can be written as

2 2 2 2 r r  perp sin  DPR  E perp E perp  (4.16) 2 2  paral cos 

Particularly for the angle, φ = 45º, the incident powers for the perpendicular and the parallel polarizations are same, thus DPR can be express as 77

0.1

2 0.08



T  0.06

0.04 600 µm 600

transmitted power, transmitted 0.02

0 0 20 40 60 80 incident angle,  [deg] i (a) (b) Figure 4.19: Decomposed plane wave incidences on a planar interface (x-y plane) at an oblique angle (θi): (a) perpendicular polarization and (b) parallel polarization

2  perp DPR  (4.17) 2  paral

For the numerical calculations, the values of the complex permittivity for healthy and cancerous tissues are extracted from the raw measurement data for a formalin-fixed liver tissue such as ϵn = 3.92 + j1.66 and ϵc = 4.25 + j2.13 around at 1THz, respectively.

Based on the material properties, the normal and cancerous tissues are also modeled, and they are simulated to examine reflection coefficients and DPRs using a commercial electromagnetic (EM) simulator, Ansoft HFSS 15 (Ansys, Inc.), which is based on the finite element method (FEM) [68]-[70]. Specifically, periodic boundary conditions with perfect matched layers (PML) [71],[72] are used to simulate the unit cells for each tissue excited by uniform plane wave, as illustrated in Fig. 4.19(a). In particular, periodic

78 boundary conditions (master/slave boundary in HFSS) along lateral direction realize infinite tissue surface illuminated by uniform plain wave. Thus the reflected field without diffraction effect at the edges of the finite tissue dimension can be obtained by PML boundary. To estimate relevant tissue thickness for negligible multiple reflection effect, various tissue thickness are tested for transmission power at 1THz. As seen in Fig.

4.19(b), less than 1% incident power is transmitted up to 86º incident angle through the tissue model with more than 600µm thickness.

1 1  (analytic)  (analytic) perp perp

 (simulated)  (simulated) 

 0.8 perp 0.8 perp

     (analytic)  (analytic) paral paral  (simulated)  (simulated) 0.6 paral 0.6 paral

0.4 0.4 reflection coefficient, coefficient, reflection reflection coefficient, coefficient, reflection 0.2 0.2

0 0 0 20 40 60 80 0 20 40 60 80 incident angle,  [deg] incident angle,  [deg] i i (a) (b) 70 20 normal (analytic) analytic

60 normal (simulated) simulated

2 

l cancerous (analytic) 15

 l

r 50

a a

cancerous (simulated) r

p 

/ 40

 p

- DPR - 10

e r

p 30

p 

20 DPR 

DPR, 5 10

0 0 20 40 60 80 0 0 20 40 60 80 incident angle,  [deg] incident angle,  [deg] i i (c) (d) Figure 4.20: Comparison of full wave simulation results with analytic calculations: (a) reflection coefficient of cancerous tissue, (b) reflection coefficient of normal tissue, (c) degree of polarization rotation (DPR) for cancerous and normal tissues, and (d) difference between DPR values of cancerous and normal tissues.

Simulation (at 1THz) results for normal and cancerous tissue with 600 µm thickness are compared to the analytic calculation results. All data demonstrate excellent agreement up to 80º incident angle, as shown in Fig. 4.20. However, the limitation of simulation accuracy for high oblique incidence angle (>80º) results in a little deviation of the reflection coefficients from the analytic results.

When most incident power in parallel polarization is transmitted into tissues, reflected power in parallel polarization, which is the denominator of DPR, is approaching to the minimum. Thus, high DPR occurs around at Brewster angle, as shown in Fig.

4.20(c). At these angles, DPR can be also significantly perturbed by the diffraction due to the discontinuity of normal-to-cancerous tissue boundary while DPRs for normal or cancerous tissues are relatively constant. Therefore, for the sensitive tumor boundary

Figure 4.21: Beam reflection at different locations around the tissue margin. The ratio of the polarization magnitude on the tissue interface is much higher compared to the homogeneous tissue areas. 80 detection, it can be efficient to utilize the incidence angle near at Brewster angle. Among the angles close to Brewster angles for normal and cancerous tissues, the maximum difference between the DPRs is exhibited at 63º, as demonstrated in Fig. 4.20(d).

The same simulation setup for Gaussian beam excitation is a more realistic scenario, as seen in Fig. 4.21. In particular, for a large-enough sample geometry, the Gaussian beam spot can avoid the external edges of the sample, minimizing the spurious edge diffraction and scattering. As a result, the tissue boundary between normal and cancerous tissue can be accurately modeled. To realize negligible multiple reflection effect, the tissue thickness was set by 600 µm, which is the same to plane-wave excitation case.

To determine the suitable incident angle for the Gaussian beam excitation, the models for normal and cancerous tissues are individually simulated at several incident angles near 63º (obtained from the case of plane wave excitation). The simulated total E- fields for perpendicular and parallel polarization are observed at the angles same to the incident angles. Each DPR value is calculated and they are compared with plane wave

(a) (b) Figure 4.22: Simulated DPR values at 60 º, 62 º, 63 º, 64 º, and 66 º incident angles for plane wave and Gaussian beam illumination 81

scan direction scan direction

healthy tissue malignant tissue malignant tissue healthy tissue

 =58 deg 12000 i 12000  =58 deg i  =59 deg

i  =59 deg 2

10000 2 10000 i



 l

 =60 deg l a

i a  =60 deg r

r i

a p

 =61 deg p E

8000 i E 8000  =61 deg 

 i



p r

6000 r 6000

 

4000 4000

DPR, DPR, 2000 2000

0 0 -1.5 -1 -0.5 0 0.5 1 1.5 -1.5 -1 -0.5 0 0.5 1 1.5 distance, [mm] distance, [mm] (a) (b) Figure 4.23: Simulated DPR values for Gaussian beam illumination as a function of scan position. (a) Healthy tissue to malignant tissue scan and (b) malignant to healthy tissue scan

excitation results, as depicted in Fig. 4.22. As a result, the simulation results are close to the results for the plane wave around at 60 º angle of incidence, and the difference of

DPR between normal and cancerous tissue is still relatively high. In light of this observation, the incident angle was set to 60 º.

Finally, a Gaussian beam is focused on the tissue surface with θi = ~ 60 º and φ =

45º. The beam is progressively scanned both from malignant tissue to healthy tissue and from healthy tissue to malignant tissue. The obtained DPR values from the simulations are shown in Fig. 4.23. As expected, much higher DPR values are exhibited near the boundary compared to the homogenous tissue areas. In particular, about 7 to 35 times and

8 to 20 times higher DPRs are detected for each scan direction.

In summary, tumor margin assessment in THz imaging can be challenging due to index contrast issues, lack of dynamic range due to highly absorptive water content, and

82 low image contrast. Among them, the low image contrast is a consequence of close optical properties between healthy and malignant tissues. As a result, the difference in the detected signals is very small, sometimes beyond the sensing capabilities of most imaging systems. However, as demonstrated here, the discontinuity at tissue boundary results in edge diffraction, and this can create a powerful effect to enhance cancer margin detection sensitivity even in very low contrast of tissue properties (i.e. 5% and 18% difference in refractive index and absorption coefficient in this simulation, respectively).

As a result, polarimetric sensing can significantly enhances differential signals coming from tissue anomalies, boundaries, tumor margin, and nerve bundles.

Furthermore, this polarimetric THz sensing scheme can easily be adapted for various in vivo medical sensing applications for preventive medicine by utilizing the probe-type sensor, as illustrated in Fig. 4.24. For instance, this type of sensor can be adopted either for skin cancer detection or endoscopic sensing application. In particular, the probe comprises of two pairs of source/detector antennas situated on the back of a hyper-hemispherical silicon lens (see Fig. 4.24(a)). First pair of sensors is made of source and detector antennas that are oriented in parallel to facilitate co-polarized sensing of the reflected signals. The second pair of sensors is oriented perpendicular to each other, allowing for the sensing of the cross-polarized component of the reflected THz wave. The sensor pairs are placed symmetrically around the quasi-optical axis and optimized to allow for accurate and high-resolution sensing of the co-polarized and cross-polarized components of the signal reflected from the same point on the sample (see Fig. 4.24(b)).

As a result, the sensor can simultaneously measure both polarization components from

(a) (b) (c) Figure 4.24: (a) components of a polarimetric THz probe, (b) A set of four objective lenses focus the two beams on the tissue, and (c) cross section of the probe

the same test spot where the spot size is extremely small (i.e. close to diffraction limited spot size), thus high spatial resolution can be achieved.

Chapter 5: THz Macromolecular Response from Abnormal Nervous System: Brain Tissue exhibiting Alzheimer’s Disease

In this chapter, we investigate the THz response and imaging of human brain tissues as an initial step to evaluate the potential of THz wave for the detection of Alzheimer’s disease. Reflection-mode time domain spectroscopy was probed into for formalin-fixed and paraffin embedded tissue specimens from hippocampus in 60GHz-2THz band.

Comparing the THz imaging with the microscopic image of hematoxylin and eosin stained tissue, we demonstrate the possibility of discriminating between major brain tissue components, including white and grey matter as based on their morphological and compositional differences. We discover that the measured THz reflection spectra, particularly from white matter, reveal detectable differences between Alzheimer’s and control tissues. Based on the clues, we establish a hypothesis for the label-free detection of Alzheimer’s disease using THz imaging. We also discuss the cause of the spectral difference through the electromagnetic simulation of a simplified model of white matter

85 neuron as well as histopathological inspection of the tissues. This study offers the possibility of diagnosing Alzheimer’s disease using THz wave, ex vivo as in this study and potentially in vivo as THz sensing technologies continue to improve.

5.1 Background on Alzheimer’s Disease

Alzheimer’s disease (AD), was first identified more than 100 years ago, and it has been the primary cause of dementia, which accounts for 60 % to 80 % of cases [73]. In

2014, it is reported that one in nine U.S. populations age 65 and older has suffered from

AD [73]. Although persistent research has promoted considerable understating of AD, much is yet to be discovered and its root cause still remains uncertain with no effective cure. One of the leading hypotheses is that the mutations of amyloid precursor protein

(APP), presenilins 1 and 2 result in senile plaques mainly consisting with a small protein called amyloid beta (Aβ42) [74], [75]. It is known that the senile plaques have been commonly found in gray matter of the brain exhibiting AD. Based on this, current pathological inspection typically characterizes AD as Aβ42 plaques buildup [76], [77], tau protein hyperphosphorylation [77], and neurofibrillary tangles (NFT) [78] in the gray matter of the brain. As a result, AD has primarily been considered a disease of gray matter.

The hypothesis, however, often miscarry to clarify important phenomena such as recent failures of clinical trials to impact dementia even after successfully diminishing

Aβ42 deposits [79], [80]. In addition, white matter neuropathological disorders, including axon loss, demyelination, and death of oligodendroglial cells, has also been suggested as

86 an important characteristic of AD based on many convergent evidences [81]. Thus, assuming the white matter defects as a biomarker of an early event in AD pathogenesis, the evaluation of white matter abnormalities is requisitely diagnosed for the early detection of AD, accompanying with Aβ42.

Besides the conventional diagnostic tools such as x-ray, CT, MRI, and positron emission tomography (PET) etc, new techniques have been investigated in research level based on the strong demands of either early or non-invasive detection of AD. For instance, optical probes in near infrared (NIR) was suggested as a candidate for quantitative Aβ42 imaging in human brain [82] and optical fiber in NIR was tested to evaluate Aβ42 in cerebrospinal fluid for the early detection of AD [83]. More recently, a molecular MRI by oligomer-specific antibodies on magnetic nanostructure was presented for the early detection of AD through Aβ oligomers (AβOs), which is also considered as a biomarker of AD [84]. However, majority of such approaches are based only on histochemical reaction to Aβ42 or AβOs in gray matter, thus essentially requiring specific fluorescences or antibodies.

Considering all neuropathological indicators, particularly Aβ42 deposits and demyelination, complex methodology is demanded for both histochemical and morphological inspections of human brain. Regarding such similar demands in bio- medical arena, time domain THz spectroscopy has recently drawn attention as a new imaging modality due to the several advantages over conventional sensing tools, as discussed in Chapter 1. As a result, a verity of researches has been explored in fundamental medical science (i.e. protein electrodynamics) as well as clinical

87 applications (i.e. cancer detection). In this regard, the properties of THz wave might be affordable to detect either the amyloid mutation or the morphological changes in AD pathogenesis.

To demonstrate this, the THz response of human brain tissues diagnosed as AD is investigated using broadband time domain THz spectroscopy in reflection mode. For the current study, we utilize formalin-fixed and paraffin-embedded (FFPE) healthy and AD tissues from the hippocampus with their associated histopathology for a direct comparative study. As demonstrated, the THz response of neuropathological changes in gray and white brain matters for samples with AD exhibits detectable differences compared to healthy control tissues. These results offer the possibility, for the first time, of detecting AD using THz imaging. Considering further case studies, we introduce a hypothesis to diagnose AD based on THz reflection spectroscopy. A simplified model of mylinated axon in white matter is also studied through 3-D electromagnetic simulation to support in part our hypothesis.

Below, we first outline our test setup and the protocols applied in tissue preparation.

Subsequently, we demonstrate the THz spectroscopic images for FFPE human brain tissues.

5.2 Sample Preparation and Experimental Setup

It is well known that the hippocampal region in brain has critical roles in the mechanisms of learning, spatial, semantic and episodic memory [85]. Thus, the hippocampus would be one of the first regions of the brain particularly vulnerable to

88 damage of degenerative processes such as AD [85], [86]. In addition, emerging evidence has revealed that dysfunctional neurogenesis in the hippocampus can be a key factor in the early AD pathogenesis, despite non-uniform hippocampal degeneration in early AD

[82]. In this regard, the hippocampus area in brain is subjected for this experimental study. The Seven brain tissues from human hippocampus particularly including CA1 region were obtained in the form of FFPE blocks from the Ohio State University Wexner

Medical Center. Among them, five are diagnosed as AD and the other two are healthy tissues. All tissue blocks were prepared by standard FFPE fixation and embedding protocol for histologic diagnosis [53]. Pathological inspection was performed at the

Department of Neuroscience at the Ohio State University Wexner Medical Center.

Identical to the previous FFPE cancer tissue studies, the entire tissue blocks processed by a medical slicer were utilized due to their fitness to our measurement topology for reflection-mode THz spectroscopy. The adjacent slices to the exposed tissue surface created by the slicer were kept for subsequent pathological assessment through hematoxylin and eosin (H&E) staining. In the experiments, THz reflection imaging was performed using the THz-TDS (TPS3000, Teraview, Ltd). As described in the Chapter

4.2., the THz beam focused onto the sample under the test had a parallel polarization, an incident angle of 30º, and diffraction limited spot size of ~360 µm at 1 THz, as depicted in Fig. 5.1(a). The mounted samples were raster-scanned by 100 µm resolution for the brain tissues.

In this experimental setup, we preliminarily test on the sample mounting method to maximize the sensitivity of spectroscopic imaging for our brain tissue study. First, the

(a) (b)

(c) (d) Figure 5.1: Description of tissue measurement setups and the corresponding THz images based on raw reflection spectra at 1.29 THz: (a) a schematic of reflection mode THz imaging, (b) the tissue placed on a z-cut quartz window with 2 mm thickness, (b) the tissue placed on metal holding frame, (c) the tissue placed on the calibrated metal holding frame to compensate slanted tissue surface. tissue block was mounted on a 2 mm thick z-cut quartz window, a typical holding material in THz band, as shown in Fig. 5.1(b). In this topology, the reflection loss from the bottom surface of the quartz is inevitable due to index difference between air (na = 1) and the quartz (nq = 2.1), thus significantly losing spectral power significantly. Moreover, the detailed tissue information in the raw THz image is masked by the gradient background originated from the non-uniform contact between the tissue and the quartz

90 due to fairly flat but slanted tissue surface. Thus, next, we imaged the tissue block placed directly on the metal holding plate to avoid the significant reflection loss, as illustrated in

Fig. 5.1(c). As expected, the raw THz image starts to describe the brain tissue morphology. However, this image still exhibits the gradient background particularly at top-left and bottom-right corners due to the slanted tissue surface. In order to resolve this issue, the elevation of the metal holding frame is adjusted to compensate the slanted angle through the line scans along both x- and y-directions. As a result, all morphologies are clearly exhibited in the obtained THz image, as demonstrated in Fig. 5.1(d). Using this mounting scheme, the scanning for all brain tissues were performed at room temperature in the nitrogen purged chamber to remove the effect of the atmospheric water vapor absorption. Each scanning took about 30 to 50 minutes depending on the tissue size. The raw reflected signal in time-domain is obtained from the measurements and though Fourier transform, the electric field responses, is computed.

5.3 THz Spectroscopic Imaging of Human Brain Tissue for

Alzheimer’s Detection

For all FFPE tissue measurements, no spectroscopic response was associated with the brain tissues in the form of unique absorption peaks in the reflection spectra. As noted above, this could be resulted from the tissue denaturalization by the formalin-fixing process. Dehydration during the paraffin-embedding process might also promote protein denaturalization. For the THz images, finer resolution can be obtained at higher frequencies due to their shorter wavelength as expected. Particularly for all our studies 91 below, the reflection spectra between 0.5 THz and 2.5 THz are integrated to construct the

THz images exhibiting the enhanced contrast in between the tissue morphologies. In addition, 20 random points in the 2D images were selected to compare the spectra of the regions of interest (such as white and gray matters) and the mean and the standard derivation were calculated.

5.3.1 THz Imaging of Alzheimer’s and Healthy Control Tissues

The optical image of the H&E stained FFPE brain tissue exhibiting AD and the corresponding THz reflectivity image are shown in Fig. 5.2. Macroscopically, the tissue histologic section includes white matter (dark pink), gray matter (white pink), and dentate gyrus. Pathological inspection classifies the tissue into AD exhibiting moderate to frequent Aβ42 plaques distribution in gray matter based on the Consortium to Establish a

Registry for Alzheimer’s Disease (CERAD). A visual comparison of the processed THz image given in Fig. 5.2(b) reveals that all morphological features of the tissue are explicitly differentiated. In particular, the white and gray matters are clearly delineated in the THz image by the degree of their reflections, respectively, as demonstrated in Fig.

5.2(c). Moreover, the subtle morphological variation such as dentate gyrus in gray matter is also marginally described.

Subsequent to the AD tissue (“AD Case 1”), two control tissues from the same region (hippocampus) in human brain were measured within two hours by the same procedure. As such, we can assume that the environmental effect on the experiment (i.e. laser power level drift and humidity variation) was negligible. According to the

(a) (b)

(c) Figure 5.2: Optical and THz reflectivity images of the FFPE brain tissue exhibiting Alzheimer’s disease: (a) microphotographs of the tissue stained with hematoxylin and eosin (H&E), (b) the integrated reflection image and (c) THz reflectivity spectra from white and gray matters of the tissues

(a) (b) (c)

(d) Figure 5.3: The integrated reflection images of (a) AD Case 1, (b) Control 1, and (c) Control 2 and (d) the ratio of reflectivity spectra between gray and white matters of the AD tissues and the controls.

pathological inspection based on the CERAD classification, whilst there is moderate-to- frequent plaque distribution in the AD tissue, “Control 1” has no plaque and “Control 2” exhibits moderate plaque distribution (but, much less than the AD tissue).

The THz images of the controls are processed and they are compared to the AD case in the same scale of the pixel intensity, as demonstrated in Fig. 5.3(a). Here, we remark

94 that, compared to the controls, THz image of the AD tissue displays much clear image contrast between the white and gray matters. This initial finding might be utilized for an independent diagnostic parameter for AD as an alternative to additional control studies.

For example, the ratio of reflection spectra from white and gray matter can be employed to figure out the degree of the image contrast such as

R White  R Gray % ratio of reflectivi ty   100 (5.1) R White

where RWhite and RGray are the reflectivity spectra from white and gray matter areas.

In this study, the percentage ratios are tabulated on by the help of the neuropathologist at OSUMC due to the ambiguity of the boundary of white and gray matters in the controls. As seen in Fig. 5.3(b), the AD tissue reveals about two times higher ratio than other two controls. Thus, we can hypothesize that brain tissues with AD exhibit distinct and consistent THz image contrast between white and gray matters. To support this, we next investigate the contrast discriminative factor between the AD and the control tissues through THz reflection spectroscopy and more case studies.

5.3.2 Origin of Prominent THz Image Contrast between White and

Gray Matters in Alzheimer’s Tissue

Given that AD would be diagnosed by examining either Aβ42 plaque deposit or white matter abnormalities, the reflection spectra from both areas in all tissues need to be compared in both gray and white matters, as shown in Fig. 5.4. For the gray matter, the

(a)

(b)

Figure 5.4: Comparison of THz reflectivity spectra between the brain tissues, AD Case 1, Control 1, and Control 2, from hippocampus: (a) gray matter and (b) white matter.

AD tissue reflects more THz signal than the controls, as demonstrated in Fig. 5.4(a). In addition, “Control 2” with sparse (moderated) plaque distribution also exhibits somewhat higher reflection spectra than “Control 1” with no plaque. Thus, even smaller difference

96 of plaque density in gray matters might be distinguishable through reflection spectroscopy.

Of course, some of such differences are within the margin of the standard deviation.

The typical dimension of the clusters of Aβ42 plaques is also in the order of tens of micrometers (i.e. 86.61 µm) beyond the THz wavelength [88], as demonstrated through our pathological inspection in Fig. 5.5. Even though it might be difficult to resolve such tiny individual plaque in THz band, the spatial distribution density of plaque buildup might be an alternate factor to determine AD based on the degree of the broad band absorption in THz band.

In the white matters, we can also detect differentiable reflection spectra similar to the gray matter comparison, as shown in Fig. 5.4(b). However, we note that the spectral

Figure 5.5: An example of amyloid beta (Aβ42) plaques in the gray matter of Alzheimer’s tissue (AD Case 1) (in-set: magnified image of a β-amyloid plaque, which exhibits neurofibrillary tangles)

97 difference between the AD and the controls is much prominent in the 0.4THz to 1THz frequency band compared to the gray case. Thus, such higher reflection from the white matter in the AD tissue contributes more to the clear image contrast than the gray matter case. Perhaps, this might be originated from the neuropathologic changes in the white matter accompanying with AD. One of the representative white matter disorders possibly connected to AD is demyelination exhibiting morphologically thinned myelin sheath around axon, and this thinned myelin sheath might cause higher reflection than the normal sheath thickness. As a result, such higher reflection results in the prominent THz image contrast between gray and white matter in AD tissues. Thus, followed by the more case studies below, we scrutinize the effect of demyelination on reflection spectra through the FEM-based full EM simulation (HFSS, Ansys, Inc.) of a simplified model of myelinated axon.

5.3.3 Case Study of Alzheimer’s Tissues in THz Imaging

Four more Alzheimer’s tissues excised from hippocampal area including CA1 region were tested over consecutive 4 hours by following the same procedure of the previous cases. Since this second set of the tissues were measured in different time, the unexpected mode change of femto-second laser source caused power level drift, however, we can still obtain the reflectivity ratio between gray and white matter, which is more valuable for our hypothesis than absolute value of reflection.

The integrated THz images of the AD tissues are displayed with the same scale of the pixel intensity in Fig. 5.6. Similar to AD case 1, clear image contrasts between gray

(a) (b)

and white matters are consistently detected on the integrated THz images at frequencies between 0.5 THz and 2.5 THz for all four brain tissues. This result still demonstrates the validity of our hypothesis.

More importantly, according to the neuropathological inspection at the OSUMC, the all AD tissues exhibit certain degree of demyelination in white matter area. In particular, as seen in Fig. 5.7, the enface AD tissue sections (10 µm slices) were processed by Luxol fast blue staining, which is typically used to observe myelin or myelin loss. In the stained images, the darker blue implies less demyelination while the lighter blue indicates more

(a) (b)

(c) (d) Figure 5.7: Microphotographs of the AD tissues processed by Luxol fast blue staining: (a) AD Case 2, (b) AD Case 3, (c) AD Case 4, and (d) AD Case 5 (red-dot-circle: hippocampus)

demyelination symptom. In order to demonstrate the degree of demyelination in each tissue, white matter areas (white circles in Fig. 5.7) are selected, and the distribution of the pixel intensities are evaluated through histogram, as shown in Fig. 5.8. The distribution presents that “AD Case 2” and “AD Case 4” exhibits the lowest and the highest degrees of demylination in the designated areas, respectively. In addition, the percentage differences of the reflection spectra from gray and white matter areas, where

100

Figure 5.8: Pixel intensity distribution of the white matter areas of the AD tissues, which are corresponding to white-circled areas in Fig. 5.7.

Figure 5.9: The ratio of reflectivity spectra between gray and white matters of the AD Cases, Control 1, and Control 2.

101 are corresponding to black and white circles in Fig. 5.7, respectively, are calculated, as demonstrated in Fig. 5.9. Here, we note that the calculated percentage differences are strongly correlated with the degree of demyelination. Specifically, the Alzheimer’s tissue exhibiting the higher percentage difference tends to include more demyelinated axon in its white matter area. Therefore, the strong image contrast between gray and white matters in Alzheimer’s tissue might be primarily originated from the white matter disorder, which is demyelination. As a result, this tendency might be utilized for defining a morphological marker to diagnose Alzheimer’s disease in early stage if more sufficient case studies reveal consistent results.

5.4 Full-EM Simulation of the Simplified Model for White

Matter Disorder

In the Section 5.3.2, we discuss about the origin of the prominent THz image contrast between gray and white matters in Alzheimer’s tissue, and we suggest that the demyelination in white matter might cause such contrast in THz images. Here, for the qualitative study of such phenomena, we investigate the effect of demyelination on reflection spectra through the FEM-based full EM simulation (HFSS, Ansys, Inc.) of a simplified model of myelinated axon. White matter is a component of the central nervous system in the brain and is mainly composed of bundles of myelinated axon, which connect the gray matter areas for the signal transfer between neurons [89]. As such, white matter can be regarded as a tight array of a myelinated axon consisting of axon, myelin sheath, oligodendrocyte, and node of Ranvier [90], as depicted in Fig. 5.10(a). In 102

(a)

(b) Figure 5.10: A description of (a) the bundles of myelinated axons in central nervous system (re-drawn based on the reference [90]) and a simplified unit cell of myelinated axon array for the EM simulation.

addition, since the number of oligodendrocyte is much less than myelin sheath [91] and node of Ranvier size (1~2 µm) is much smaller than THz wavelength [90], the structure of myelinated axon can be further simplified into the array of a coaxial cable in THz band, as shown in Fig. 5.10(b).

In general, the actual dimensions of axon and myelin sheath are varied by species and the location of nerve cells. However, the g-ratio (typically ~0.6), which is the ratio between the radius of axon and the thickness of myelin sheath, is commonly accepted in contemporary physiology [90], [92]. Thus, based on the reference [93], the unit cell (see

103

Fig. 5.10(b)) is modeled by the g-ratio with 5µm for the radius of axon and 3.4µm for the thickness of myelin sheath. In Cartesian coordinate, 30 layers of the unit cell are stacked in z-direction to consider multiple reflections between the layers. In addition, periodic boundary conditions are defined for x- and y-directions to realize laterally infinite array of myelinated axons, and Floquet ports are applied to monitor the reflection from this model.

Unfortunately, it is rare to define the electrical properties such as dielectric constants and absorptions of axon and myelin because of their complex constituents and in-homogeneities in extremely tiny membrane. In addition, although several studies found their dielectric constants, resistivities, capacitances, and conductivities, they utilized DC or very low frequency (~kHz) circuit models to calculate the properties [93],

[94]. Therefore, it is inappropriate to apply them to our simulation in THz band.

However, we remark the common findings of the all previous studies that myelin sheath typically exhibits low dielectric constant closed to the value of lipid content and axon has higher dielectric constant. Furthermore, the axon is commonly represented as a cylindrical cable (such as our model) filled with an electrolyte solution in medical physiology [95]. Following these, the dielectric constants (ε) and loss tangents (ta δ) of lipid content [96] and electrolytes in aqueous Luria-Bertani media [97] are adopted into the simulation through the Debye model, as tabulated in Table 2.

In the simulation, the myelin thickness is decreased to 75%, 50%, and 25% of the normal thickness so that we can monitor the effect of the thickness variation in 0.2THz to

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Table 2: Material properties of lipid content [96] and electrolytes in aqueous Luria-Berani media [97] for the Debye Model

Properties Lipid Electrolytes in Aquoues Luria-Berani Media

f1 200 GHz

f2 1200 GHz

ε1 2.25 14.82

ε2 2 5.76

ta δ1 0.048 0.298

ta δ2 0.039 0.464

1.2THz frequency band. Then, we calculate the percentage difference of the simulated reflections for normal and demyelinated axons such as

 Demyelinat ion   Normal % difference   100 (5.2)  Demyelinat ion

where ΓDemyelinated and ΓNormal are the simulated reflection coefficient for the normal and the demyelinated axons, respectively. Subsequently, the results are compared with the measurement results, as demonstrated in Fig. 5.11. The simulations result in higher reflections for demyelinated axons, similar to the measurements. In addition, as the thickness of myelin sheath become thinner, more reflection is exhibited. Even though the specific values can be different from actual cases due to the variations in size and material properties of the tissues, these phenomena are the good clue to explain the origin of the prominent reflection contrast in white matter. Furthermore, this might be a biomarker to detect AD in THz band.

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Figure 5.11: Comparison of THz reflectivity spectra between the brain tissues, AD Case 1, Control 1, and Control 2, from hippocampus: (a) gray matter and (b) white matter

We demonstrate the feasibility of THz imaging toward AD detection by studying seven tissues excised from hippocampal areas in human brains, which might be one of the first regions affected by degenerative processes. A commercially-available THz time- domain spectroscopy system was utilized for the reflection mode spectroscopy and images of an AD and two control tissues. Through the comparison between them, we hypothesize that clear and consistent image contrast between gray and white matters is prominent in AD tissue unlike healthy brain tissues. The origin of the image contrast is speculated through their reflection spectroscopy, pathological inspection, and simulation of a simplified model for myelinated axon. More case studies were performed through four more AD tissues from hippocampus, which provided the validity of our hypothesis.

106

This study reveals the strong correlation between AD and THz image contrast, thus this might be useful for defining a biomarker to diagnose AD in early stage. Of course, there is no doubt that further case studies including fresh brain tissues are required together with more detailed tissue information such as the degree of plaque distribution and demyelination.

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Chapter 6: Conclusion

6.1 Summary of this Work

We present a comprehensive study of THz spectroscopy and imaging, developed by the experiments on bio-chemicals, animal tissues, major human organ tissues, and associated diseases. Following the recent development of photoconductive technologies, the nature of THz wave has been revaluated in many contemporary researches depart from the traditional astronomic subject. Among them, particular interest has been arising in bio-medical science due to the advantages of THz imaging over the conventional imaging modalities. First and foremost, THz radiation exhibits significantly lower energies compared to X-rays, thus being a safe clinical diagnostic tool. Also, the characteristic modes of macromolecules are occurred at THz frequencies either by their rotational or vibrational behaviors. Moreover, sub-millimeter-scale image resolution of

THz waves is capable to describe various types of morphological lesions. More importantly, high sensitivity of THz wave to hydration can be also easily applicable to

108 current and future bio-sensing mechanisms. In this study, the critical properties of THz wave applicable to bio-medical sensing were verified and employed to explore new possible utilities of THz spectroscopic imaging.

Specifically, THz time domain spectroscopy (TDS) system was mainly utilized for our experimental studies. Two standard chemicals (α-lactose monohydrate and biotin) exhibited their characteristic modes in THz band and three liquids (distilled water, 10% buffered formalin, and methanol) were investigated. Through this, the specificity of the

TDS system and the accuracy of the algorithm to extract optical properties were validated. The experiments on a fresh hibiscus leaf and an animal liver tissue demonstrated the sensitivity of THz wave to hydration in THz imaging.

The characterization of fresh human organ tissues excised from a stomach, a heart, and a pancreas exhibited differentiable optical properties of the human tissues in THz band as well as associated contrast in the THz image. Following the human tissue characterization, THz imaging was performed on formalin-fixed human liver tissue including metastatic malignant tumor adjacent to healthy region. The obtained image of the liver tissue, performed with the appropriate tissue mounting on the experiment, displayed the clear description of the cancer margin.

The experimental studies presented above clearly proved the applicability of THz wave to bio-medical sensing. Based on the fundamental studies, we demonstrated the utility of THz imaging for endoscopic assessment of potential malignancies by studying two cancerous tissues excised from respiratory and gastrointestinal tracts that are readily accessible via a thin endoscope. Considering the topology of the endoscopic sensor, a

109 polarimetric sensing scheme was proposed for more sensitive cancer margin identification, and its performance was demonstrated by electromagnetic simulation.

Motivated by the pathogenesis of Alzheimer’s disease related to histochemical and morphological changes in human brain, THz spectroscopic imaging of human brain tissues is carried out for the first time. We demonstrated a prominent contrast of THz reflectivity from gray and white matter in the brain tissue exhibiting Alzheimer’s disease, compared to healthy brain tissue. The origin of the contrast was discussed through THz reflection spectra and the simulation of the simplified model of myelinated axon, which is main component of white matter. Overall, it is demonstrated that key discriminatory information can be achieved from the THz spectroscopy and images in all case studies regarding from healthy tissues to central nervous systems.

6.2 Future Work

In the Chapter 5.4, the simplified model of myelinated axon is simulated to evaluate the effect on demyelination to reflection from white matter in Alzheimer’s tissue. Possibly, more accurate model of myelinated axon enables in-depth understating of the relation between higher reflection from white matter and Alzheimer’s disease.

Again, such understanding might allow establishing a new morphological marker to diagnose Alzheimer’s disease in early stage. Thus, we start this section with more consideration to improve our axon model.

In addition, given the current measurement topologies either in transmission and reflection mode spectroscopy, certain type of sample holding apparatus (i.e. quartz

110 window in our studies) is required for non-solid materials such as biological tissues, powered and liquid samples. However, in this scenario, the sensitivity degradation of

THz wave is unavoidable due to the reflection loss and material loss from the holding apparatus. Additionally, in most commercially-available THz spectroscopic systems, the measurements are performed through the far field detection, thus the typical diffraction limited spot size is in the order of few hundred micrometers. However, dimensions of bio-molecules are usually much less than the diffraction limited spot size [33]. For example, protein crystals have about 100 µm dimensions [33]. As a result, even except for the line broadening effect of hydration to the characteristic sharp absorption spectra of certain biomolecules, such far-field based spectroscopy marginally resolves the sharp absorption lines. To resolve or at least to mitigate these issues, artificially constructed metamaterials can be utilized due to their engineered electromagnetic responses. Thus, some future research topics to enhance the measurement sensitivity and to achieve sub- wavelength spectroscopic resolution for bio-medical sensing are considered.

6.2.1 Further Considerations of Myelinated Axon Model for EM

Simulation

As mentioned in the Chapter 5.4, the myelinated axon is modeled as the periodic array of coaxial structure based on several assumptions and data (dimensions and material properties) from the references. However, due to the nature, the actual myelinated axons are arranged non-periodically, and the actual dimension of axons and

111

(a)

(b) Figure 6.1: Microscope images of myelinated axons [98]: (a) top-view of myelinated axon in central nervous system (blue arrows: oligodendroscytes and yellow arrows (astroscytes) and (b) cross sectional view of myelinated axons in peripheral nervous system.

112 myelin sheath exhibit random values in certain range, as demonstrated in Fig. 6.1. In this circumstance, we need to include such variations in the axon model for more accurate simulation. For this, considering the raster-scanned image step size (100µm) with the diffraction limited spot size (~ 360 µm at 1 THz) of our system, we can define a bundle of randomly spaced axons as a unit cell for the simulation.

Since the actual dimension of axons can be varied in each individual as well as the location of the tissues, microscopic inspection of myelinated axons in the measured tissues can be useful to estimate the axon dimension more accurately. Furthermore, to mimic the TDS measurement scenario, Gaussian beam can be utilized in different incident angles for the simulation.

6.2.2 THz Transparent Metamaterials for Enhanced Spectroscopy and

Imaging

As described in the Chapter 5.2, the losses of the spectral power due to sample holders (such as quartz) are very critical in THz bio-medical sensing based on current commercially available THz systems, typically adopting low power THz source. To alleviate this problem, THz transparent metamaterials can be utilized to hold the non- solid samples instead of the z-cut quartz window, thus enhancing the sensitivity of THz spectroscopy and imaging, as demonstrated in our previous designs [99], [100].

In general, metamaterials and periodic structures exhibit novel modes that provide new functionality and much needed control of electromagnetic wave propagation. Based their exotic properties such as negative index [101],[102], metamaterials have been

113

(a) (b) (c) Figure 6.2: Geometry of the circular slot FSS unit cell [100] (a) Top view (b) Single Layer Cross- section (c) With superstrate Cross-section

proposed and studied for cloaking [103], [104], miniature antennas [105], and solar cells

[106] etc. in various frequency band. Among them, impedance matched surfaces could be considered as one option in designing THz sample holder. Several designs of the impedance matched surface in high frequency have been studied so far [107]-[101], however, none of them provide broadband transmission response, which is required for our spectroscopic analysis. In addition, the thin thickness of those designs is too fragile to hold biological samples for THz bio-medical sensing.

To realize the broadband performance as well as relevant thickness to hold the samples, multi-layered periodic surfaces utilizing both substrate and superstrate can be considered, as shown in Fig. 6.2 [100]. In our initial designs, by adjusting the geometry and spacing of the unit cells, desired effective refractive index as well as transmission and reflection behaviors can be tailored. Moreover, the broadband near perfect transmission performance can be attainable by adopting superstrate on the top of unit cell patterns, as shown in Fig. 6.3. As such, more sensitive spectroscopic image can be

114

(a) (b)

(c) (d) Figure 6.3: Simulated transmission response of the circular slot FSS window and double square loop FSS window for different angle of incidence (θ) [100]: (a) circular slot FSS window (b) circular slot FSS window with superstrate, (c) double square loop FSS window, and (d) circular slot FSS window with superstrate. obtained with the THz transparent metamaterials compared to other sample holding materials, as demonstrated in Fig. 6.4.

Of course, in these particular designs, we cannot avoid the reflection loss occurred at between air and the substrate since the quartz substrate exhibiting similar refractive index to z-cut quartz (nq = 2.1) was used for the design and fabrication. However, considering other materials with closer reflective indices to air (na = 1) for the substrate in the

115

(a) (b) (c) (d) Figure 6.4: (a) Visual image of a coin is being compared with its THz images (using TPS300 spectroscopy) on (b) conventional glass slide, (c) z-cut quartz (d) double square loop FSS window as sample holder in grayscale [100]

metamaterials design, we can also significantly reduce the reflection loss, thus saving more spectral power to characterize bio-subjects.

6.2.3 Detection of Macromolecular Characteristic Absorptions in

enhanced Near-field THz Spectroscopy

Previous experiment of the hydrated α-lactose monohydrated power demonstrated that the lowest vibrational absorption around at 530 GHz is still dominant despite of the line broadening effect of hydration on spectral absorption. Also, several contemporary studies also represent the low-frequency vibrational mode of protein within THz regime though the experimental on met-hemoglobin and chicken egg white lysozyme [33], [41].

However, a recent our study of human bloods exhibiting different level of glycated hemoglobins exhibit no vibrational absorption peaks for hemoglobin in THz spectroscopy as well as almost same reflectivity between the samples, as shown in Fig. 6.5.

As noted above, this might be explained by higher diffraction limited spot size of our system compared to the protein crystal or molecular dimensions. Thus, to overcome

116

Figure 6.5: Reflectivity of distilled water and liquid human blood exhibiting six levels of HbA1C

this, THz spectroscopy with sub-wavelength resolution is needed, similar to near-field microscopy in [112]. For this, superlens [113], metamaterials exhibiting negative index, can be utilized to the current far-field detection system. The three types of superlens have been reported in recent researches such as near-field superlens, far-field superlens, and hyperlens, as illustrated in Fig. 6.6 [114]. In general, the large feature information is typically carried by the propagating waves, which can be arrived to far field; however, the fine feature information is carry by evanescent wave confined to the near field.

Using a conventional lens, the evanescent waves are decaying rapidly before the image plane. However, the evanescent waves can be enhanced though the superlens, thus obtaining the sub-wavelength scale resolution, as shown in Fig. 6.6(b). Moreover, one

117

(a) (b)

(c) (d) Figure 6.6: Comparison of various types of optical lenses [114]. (a) conventional lens, (b) near-field superlens, (c) far-field superlens, and (d) hyperlens. Blue and red curves represent propagating waves and evanescent waves, respectively.

can obtain the fine feature information even through the far-field detection using far-field superlens and hyperlens, as depicted in Fig. 6.6(c) and (d).

For the bio-medical sensing based current THz-TDS system, the far-field superlens or hyperlens might be strong candidates for the enhancement of spectroscopic image resolution. Therefore, leveraged by such superlens, the low frequency vibrational modes of various bio-molecules might be characterized in THz regime. For example, much smaller feature resulted from bio-chemical mutation of Alzheimer’s disease such as single amyloid beta plaque (~50 µm) might be detected through signature-type THz absorption spectroscopy as well as THz imaging. Thus, such gray matter disorder can be also used as a bio-marker in THz Alzheimer’s detection along with the morphological change of myelin sheath in white matter, as discussed in the Chapter 5.3. Moreover, the 118 superlense topology is compatible with the objective lens of the polarimetic endoscope sensor proposed in the Chapter 4.5.

119

Bibliography

[1] D. H. Auston, “Picosecond optoelectronic switching and gating in silicon,” Applied Phys. Lett., vol. 26, no. 3, pp. 101-103, 1975.

[2] P. LeFur and D. H. Auston, “A kilovolt picosecond optoelectronic switch and Pockel’s cell,” Appl. Phys. Lett., vol. 28, no. 1, pp. 21-23, Jan. 1976.

[3] B. Ferguson and X. C. Zhang, “Materials for terahertz science and technology,” Nat. Mater., vol. 1, no. 1, p. 26, Sep. 2002.

[4] J. F. Federici, B. Schulkin, F. Huang, and D. Gary, “THz imaging and sensing for security applications-explosives, weapons and drugs,” Semicond. Sci. Tech., vol. 20, no. 7, p. S266, Jul. 2005.

[5] J. S. Melinger, Y. Yang, M. Mandehgar, and D. Grischkowsky, “THz detection of small molecule vapors in the atmospheric transmission windows,” Opt. Express, vol. 20, no. 6, pp. 6788-6807, Mar. 2012.

[6] J. C. G. Lesurf, "8.3. 'Sky' Noise," Internet: www.st- andrews.ac.uk/~www_pa/Scots_Guide/RadCoM/part8/page 3.html, [Accessed: Mar. 27, 2015].

[7] A. I. McIntosh, B. Yang, and S. M. Goldup, “Terahertz spectroscopy: a powerful new tool for the chemical sciences?,” Chem. Soc. Rev., vol. 41, no. 6, pp. 2072-2082, Dec. 2011.

[8] J. Alton and M. Igarashi, “Non-destructive fault localization in advanced IC packages using electro optical terahertz pulse reflectometry,” in EMPC, 2013, pp. 1-4.

120

[9] T. Kiwa, M. Tonouchi, M. Yamashita, and K. Kawase, “Laser terahertz-emission microscope for inspecting electrical faults in integrated circuits,” Optics Letters, vol. 28, no. 21, pp. 2058-2060, Nov. 2003.

[10] K. B. Cooper, R. J. Dengler, N. Llombart, B. Thomas, G. Chattopadhyay, and P. H. Siegel, “THz Imaging Radar for Standoff Personnel Screening,” IEEE Trans. THz Sci. Technol., vol. 1, no. 1, pp. 169-182., Aug. 2011.

[11] Y. C. Shen and P. F. Taday, “Development and application of terahertz pulsed imaging for nondestructive inspection of pharmaceutical tablet,” IEEE J. Sel.Topics Quantum Electron.,14(2), pp. 407-415, Apr. 2008.

[12] M. Tonouchi, “Cutting-edge terahertz technology,” Nature photon., vol. 1, pp. 97- 105, Feb. 2007.

[13] J. B. Jackson, M. Mourou, J. F. Whitaker, and I. N. Duling, “Terahertz imaging for non-destructive evaluation of mural paintings,” Opt. Commun., vol. 281, pp. 527-532, Feb. 2008.

[14] T. F. Filtenborg, “Analysis of a 17C Panel Painting by Reflection Terahertz Time Domain-Imaging (THz-TDI): contribution of ultrafast optics to museum collections inspection,” IEEE Signal Process. Mag., 2015.

[15] P. C. Ashworth and E. Pickwell, “Terahertz pulsed spectroscopy of freshly excised human breast cancer,” Opt. Express., vol. 17, no. 15, pp. 12444-12454, Jul. 2009.

[16] R. M. Woodward, V. P. Wallace, R. J. Pye, and B. E. Cole, “Terahertz pulse imaging of ex vivo basal cell carcinoma,” J. Invest. Dermatol., vol. 120, pp. 72-78, Jan. 2003.

[17] S. Y. Huang, Y. Wang, and D. Yeung, “Tissue characterization using terahertz pulsed imaging in reflection geometry,” Phys. Med. Biol., vol. 54, pp. 149-160, Dec. 2009 121

[18] A. J. Fitzgerald, E. Berry, and N. N. Zinov'ev, “Catalogue of human tissue optical properties at terahertz frequencies,” J. Bio.Phys., vol. 129, pp. 123-128, Jun. 2003.

[19] Z. D. Taylor, R. S. Singh, and D. B. Bennett, “THz medical imaging: in vivo hydration sensing,” IEEE Tans. THz Sci. Technol., vol. 1, pp. 201-219, Aug. 2011.

[20] M. Schwerdtfeger, S. Lippert, and M. Koch, “Terahertz time-domain spectroscopy for monitoring the curing of dental composites,” Biomed. Opt., vol. 3, no. 11, pp. 2842-2850, Oct. 2012.

[21] M. H. Arbab, D. P. Winebrenner, T. C. Dickey, A. Chen, M. B. Klein, and P. D. Mourad, “Terahertz spectroscopy for the assessment of burn injuries in vivo,” J. Biomed. Opt., vol. 18, no. 7, pp. 077004-077004, Jul. 2013.

[22] C. B. Reid, A. Fitzgerald, G. Reese, R. Goldin, P. Tekkis, P. S. O'Kelly, E. Pickwell- MacPherson, A. P. Gibson, and V. P. Wallace, “Terahertz pulsed imaging of freshly excised human colonic tissues,” Phys. Med. Biol., vol. 56, no. 14, pp. 4333-53, Jun. 2011.

[23] S. Sy, S. Huang, Y. Wang, J. Yu, and A. T. Ahuja, “Terahertz spectroscopy of liver cirrhosis: investigating the origin of contrast,” Phy. Med. Biol., vol. 55, pp. 7857- 7596, Nov. 2010.

[24] E. Pickwell and V. P. Wallace, “Biomedical applications of terahertz technology,” J. Phys. D: Appl. Phys., vol. 39, pp. R301-R310, Aug.2006.

[25] S. Nakajima, H. Hoshina, M. Yamashita, C. Otani, and N. Miyoshi, “Terahertz imaging diagnostics of cancer tissues with a chemometrics technique,” Appl. Phys. Lett., vol. 90, p. 041102, Jan. 2007.

[26] F. Formanek, M. A. Brun, and A. Yasuda, “Contrast improvement of terahertz images of thin histopathologic sections,” Biomed. Opt. Express, vol. 2, no. 1, pp. 58- 64, Jan. 2011. 122

[27] C. H. Zhang, G. F. Zhao, B. B. Jin, Y. Y. Hou, and H. H. Jia, “Terahertz imaging on subcutaneous tissues and liver inflamed by liver cancer cells,” Terahertz Sci. Tech., vol. 5, no. 3, pp. 114-123, Sep. 2012.

[28] J. Nishizawa, T. Sasaki, K. Suto, T. Yamada, T. Tanabe, T. Tanno, T. Sawai, and Y. Miura, “THz imaging of nucleobases and cancerous tissue using a GaP THz-wave generator,” Opt. Commun., vol. 244, pp, 469-474, Jan. 2005.

[29] W. G. Yeo, N. K. Nahar, and J. L. Volakis, “Validation of CW THz spectral measurements,” in IEEE APSURSI, 2012, pp. 1-2.

[30] G. M. Png, J. W. Choi, B. W. Ng, S. P. Mickan, D. Abbott, and X. C. Zhang, “The impact of hydration changes in fresh bio-tissue on THz spectroscopic measurements.,” Phys Med Biol, vol. 53, no. 13, pp. 3501-3517, Jun. 2008.

[31] H. Hoshina, A. Hayashi, and N. Miyoshi, “Terahertz pulsed imaging of frozen biological tissues,” Appl. Phys. Lett., vol. 91, no. 12, pp. 123901, Mar. 2009.

[32] Y. C. Sim, J. Y. Park, K. M. Ahn, and C. Park, “Terahertz imaging of excised oral cancer at frozen temperature,” Biomed. Opt. Express, vol. 4, no. 9, pp. 1413-1421, Jul. 2013.

[33] G. Acbas, K. A. Niessen, E. H. Snell, and A. G. Markelz, “Optical measurements of long-range protein vibrations,” Nat. Commun., vol. 5, pp. 1-7, Jan. 2014.

[34] C. Zhang, E. Tarhan, and A. K. Ramdas, “Broadened far-infrared absorption spectra for hydrated and dehydrated myoglobin,” J. Phys. Chem. B., vol. 108, pp. 10077- 10082, Jun. 2004.

[35] J. Xu, K. W. Plaxco, and S. J. Allen, “Collective dynamics of lysozyme in water: terahertz absorption spectroscopy and comparison with theory,” J. Phys. Chem. B, vol. 110, pp. 24255-24259, Nov. 2006.

123

[36] G. M. Png, J. W. Choi, and I. Guest, “Molecular and structural preservation of dehydrated bio-tissue for THz spectroscopy,” Proc. of SPIE, vol. 6146, pp. 64160W- 1-9, 2006.

[37] P. Knobloch and C. Schildknecht, “Medical THz imaging: an investigation of histopathological samples,” Phys Med Biol, vol. 47, no. 21, pp. 3875-3884, Nov. 2002.

[38] M. Brucherseifer, M. Nagel, P. H. Bolivar, and H. Kurz, “Label-free probing of the binding state of DNA by time-domain terahertz sensing,” Appl. Phys. Lett., vol 77, no. 24, pp. 4049-4051, Dec. 2000.

[39] A. Rahman, B. Stanley, and A. K. Rahman, “Ultrasensitive label-free detection and quantitation of DNA hybridization via terahertz spectrometry,” Proc. of SPIC, vol. 7568, pp. 756810-1-756810-7, 2010.

[40] A. G. Markelz, A. Roitberg, and E. J. Heilweil, “Pulsed terahertz spectroscopy of DNA, bovine serum albumin and collagen between 0.1 and 2.0 THz,” Chem. Phys. Lett., vol. 320, pp. 42-48, Mar. 2000.

[41] O. Gurel, J. Park, and S.-E. Ryu, "Terahertz spectroscopy of methemoglobin: implications for novel medical imaging and therapeutics," in SPIE Photonics West BiOS, 2013.

[42] D. W. Porterfield, “High-efficiency terahertz frequency triplers,” in IEEE Microwave Symposium, 2007, pp. 337-340.

[43] A. Roggenbuck, H. Schmitz, and A. Deninger, “Coherent broadband continuous- wave terahertz spectroscopy on solid-state samples,” New J. Phys., vol. 12, p. 043017, Apr. 2010.

[44] E. R. Brown, J. E. Bjarnason, and A. M. Fedor, “On the strong and narrow absorption signature in lactose at 0.53 THz,” Appl. Phys. Lett., vol. 90, pp. 061908, Feb. 2007. 124

[45] M. C. Hamilton, R. A. Hites, and S. J. Schwager, “Lipid composition and contaminants in farmed and wild salmon,” Environ. Sci. Technol., vol. 39, pp. 8622- 8629, Oct. 2005.

[46] J. Exler and P. R. Pehrsson, “Nutrient content and variability in newly obtained salmon data for USDA Nutrient Database for Standard Reference,” FASEB J., Apr. 2007.

[47] A. G. Davies, A. D. Burnett, W. Fan, and E. H. Linfield, “Terahertz spectroscopy of explosives and drugs,” Mater. Today, vol. 11, no. 3, pp. 18-26, Mar. 2008.

[48] T. M. Korter and D. F. Plusquellic, “Continuous-wave terahertz spectroscopy of biotin: vibrational anharmonicity in the far-infrared,” Chem. Phys. Lett., vol. 385, pp. 45-51, Feb. 2004.

[49] D. G. Allis, A. M. Fedor, T. M. Korter, and J. E. Bjarnason, “Assignment of the lowest-lying THz absorption signatures in biotin and lactose monohydrate by solid- state density functional theory,” Chem. Phys. Lett., vol. 440, pp. 203-209, Jun. 2007.

[50] J. E. Bertie and Z. Lan, “Infrared Intensities of Liquids XX: The Intensity of the OH Stretching Band of Liquid Water Revisited, and the Best Current Values of the -1 Optical Constants of H2O(l) at 25°C between 15,000 and 1cm ," Appl. Spectrosc., 50, no. 8, pp. 1047-1057, Mar. 1996.

[51] J. Xu, K. W. Plaxco, and S. J. Allen, “Collective dynamics of lysozyme in water: terahertz absorption spectroscopy and comparison with theory,” J. Phys. Chem. B, vol. 110, pp. 24255-24259, Nov. 2006.

[52] Y. Sun and B. M. Fischer, “Effects of formalin fixing on the terahertz properties of biological tissues,” J. Biomed. Opt., vol. 14, no. 6, pp. 064017-1-064017-7, Nov./Dec. 2009.

125

[53] Protocols Online, “Paraffin Processing of Tissue,” protocolonline.com. [Online].Available:http://protocolsonline.com/histology/sample-preparation/paraffin- processing-of-tissue/ [Accessed: Sep. 26, 2014]

[54] A. M. Hassan, D. C. Hufnagle, M. El-Shenawee, and G. E. Pacey, “Terahertz imaging for margin assessment of breast cancer tissues,” in IEEE MTT, 2012.

[55] M. A. Brun, F. Formanek, and A. Yasuda, “Terahertz imaging applied to cancer diagnosis,” Phys. Med. Biol., vol. 55, no. 16, pp. 4615-4623, Jul. 2010.

[56] E. P. J. Parrott, S. M. Y. Sy, T. Blu, V. P. Wallace, and E. Pickwell-MacPherson, “Terahertz pulsed imaging in vivo: measurements and processing methods,” J. Biomed. Opt., vol. 16, no. 10, 106010, Oct. 2011.

[57] I. Echchgadda, J. A. Grudt, M. Tarango, B. L. Ibey, T. Tongue, M. Liang, H. Xin, and G. J. Wilmink, “Using a portable terahertz spectrometer to measure the optical properties of in vivo human skin,” J. Biomed. Opt., vol. 18, no. 12, 120503, Dec. 2013.

[58] D. Bennett, Z. Taylor, P. Tewari, S. Sung, A. Maccabi, R. Singh, M. Culijat, W. Grundfest, J.-P. Hubschman, E. Brown, “Assessment of corneal hydration sensing in the terahertz band: in vivo results at 100GHz,” J. Biomed. Opt., vol. 17, pp. 097008, Sep. 2012.

[59] I. Stainvas and D. Lowe, “A Generative Model for Separating Illumination and Reflectance from Images,” J. Mach. Learn. Res., vol. 4, pp. 1499-1519, Dec. 2003.

[60] K. Zuiderveld, Contrast limited adaptive histogram equalization. Academic Press Professional, Inc., 1994, pp. 474-485.

[61] J. C. Russ, The Image Processing Handbook, 2nd ed., CRC Press, 2002.

126

[62] S. M. Pizer, E. P. Amburn, J.D. Austin, R. Cromartie, A.Geselowitz, T. Greer, B.M. ter Haar Romeny, J.B. Zimmerman, and K. Zuiderveld, “Adaptive Histogram Equalization and its Variations,” Comput. Vision Graphics, vol. 39, pp. 355–368, 1897

[63] A. M. Reza, “Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement,” J. of VLSI Sig. Proc., vol. 38, pp. 35-44, Aug. 2004.

[64] R. C. Gonzalez and P. A. Wintz, Digital image processing, Addison-Wesley, 1993.

[65] R. Milo and R. phillips, “Cell Biology by the Numbers,” book.bionumbers.org. [Online]. Available: http://book.bionumbers.org/how-big-is-a-human-cell/ [Accessed: May. 2, 2015]

[66] E. Pickwell-MacPherson, "Practical Considerations for in Vivo THz Imaging," Terahertz Science and Technology, vol. 3, no. 4, pp. 163-171, Dec. 2010.

[67] C. A. Balanis, ”Reflection and Transmission” in Advanced Enginnering electomagnetics, John Wiley & Sons, Inc., 1989, pp. 185-193.

[68] T. F. Eibert, K. Sertel, and J. L. Volakis, "Hybrid finite element modeling of conformal antenna and array structures utilizing fast integral methods," Int. J. Num. Model., vol. 13, pp. 81-101, 2000.

[69] M. Vouvakis, Z. Cendes, and J. F. Lee, "A FEM Domain Decomposition Method for Photonic and Electromagnetic Band Gap Structures," IEEE Trans. Antennas Propag., vol. 54, no. 2, pp. 721-733, Feb. 2006.

[70] F. L. Teixeira, "Time-domain finite-difference and finite-element methods for Maxwell equations in complex media," IEEE Trans. Antennas Propag., vol. 56, no. 8, pp. 2150-2166, Aug. 2008.

127

[71] J. P. Berenger, "A perfectly matched layer for the absorption of electromagnetic waves," J. Comp. Phys., vol. 114, pp. 114-185, 1994.

[72] F. L. Teixeira and W. C. Chew, "Complex space approach to perfectly matched layers: a review and some new developments," Int. J. Num. Model., vol. 13, pp. 441- 455, 2000.

[73] Alzheimer’s Association, “2014 Alzheimer’s disease facts and figures,” Alzheimers Dement., vol. 10, no. 2, pp. e-47-e92, Mar. 2014.

[74] S. C. Waring and R. N. Rosenberg, “Genome-Wide Association Studies in Alzheimer Disease,” Arch. Neurol., vol. 65, no. 3, pp. 329-334, Mar. 2008.

[75] D. J. Selkoe, “Translating cell biology into therapeutic advances in Alzheimer’s disease,” Nature, vol. 399, pp. A23-A31, Jun. 1999.

[76] J. Hardy and D. Allsop, “Amyloid Deposition as the Central Event in the Aetiology of Alzheimer’s Disease,” Trends Pharmarcol. Sci., 12(10), pp. 383-388, Oct. 1991.

[77] A. Mudher and S. Lovestone, “Alzheimer’s disease – do tauists and Baptists finally shake hands?,” Trands Neurosci., 25(1), pp. 22-26, Jan. 2002.

[78] M. Goedert, M. G. Spillantini, and R. A. Crowther, “Tau Proteins and Neurofibrillary Degeneration,” Brain Pathol., 1(4), pp. 279-286, Jul. 1991.

[79] G. Bartzokis, “Alzheimer’s disease as homeostatic responses to age-related myelin breakdown,” Neurobiol. Aging, 32(8), pp. 1341-1371, Sep. 2011.

[80] G. Bartzokis, P. H. Lu, and J. Mintz, “Human brain myelination and amyloid beta deposition in Alzheimer’s disease,” Alzheimers Dement., 3(2), pp. 122-125, Apr. 2007.

[81] P. S. Sachdev, L. Zhuang, N. Braidy, and W. Wen, “Is Alzheimer’s a disease of the white matter?,” Curr. Opin. Phychiatry, 26(3), pp. 244-251, May 2013. 128

[82] S. B. Raymond, J. Skoch, I. D. Hills, E. E. nesterow, T. M. Swager, and B. J. Bacskai, “Smart optical probes for near-infrared fluorescence imaging of Alzheimer’s disease pathology,” Eur. J. Nucl. Med. Mol. Imaging, vol. 35, pp. S93-S98, Mar. 2008.

[83] O. Harbater and I. Gannot, “An optical method for early detection of Alzheimer’s disease,” SPIE newsroom, 2015.

[84] K. L. Viola, J. Sbarboro, R. Sureka, M. De, M. A. Bicca, J. Wang, S. Vasavada, S. Satpathy, S. Wu, H. Joshi, P. T. Velasco, K. MacRenaris, E. A. Waters, C. Lu, J. Phan, P. Lacor, P. Prasad, V. P. Dravid, and W. L. Klein, “Towards non-invasive diagnostic imaging of early-stage Alzheimer’s disease,” Nat. Nanotechnol., vol. 10, pp. 91-98, Dec. 2014.

[85] Y. Mu and F. H. Gage, “Adult hippocampal neurogenesis and its role in Alzheimer’s disease,” Mol. Neurodegener., vol. 6, no. 85, pp. 1-9. Dec. 2011.

[86] M. Padurariu, A. Ciobica, I. Mavroudis, D. Fotiou, and S. Baloyannis, “Hippocampal Neuronal Loss in the CA1 and CA3 Areas of Alzheimer’s Disease Patients,” Psychiatr. Danub., vol. 24, no. 2, pp. 152-158, Jun. 2012.

[87] G. A. Kerchner, G. K. Deutsch, M. Zeineh, R. F. Dougherty, M. Saranathan, and B. K. Rutt, “Hippocampal CA1 apical neuropil atrophy and memory performance in Alzheimer’s disease,” Neuroimage, 63(1), pp. 194-202, Jul. 2012.

[88] A. Serrano-Pozo, M. L. Mielke, A. Muzitansky, T. Gomez-Isla, J. H. Growdon, B. J. Bacskai, R. A. Betensky, M. P. Frosch, and B. T. Hyman, “Stable Size Distribution of Amyloid Plaques Over the Course of Alzheimer Disease,” J. Neuropathol. Exp. Neurol., 71(8), pp. 694-701, Aug. 2012.

[89] M. A. Patestas and L. P. Gartner, A Textbook of Neuroanatomy. John Wiley & Sons, 2013.

129

[90] K. Susuki, “Myelin: A Specialized Membrane for Cell Communication,” Nature Education, 3(9):59, 2010.

[91] F. A. Khan, Biotechnology in Medical Sciences. CRC Press, 2014.

[92] T. Paus and R. Toro, “Could sex differences in white matter be explained by g ratio?,” Front. Neuroanat., vol. 3, 014, Jun. 2009.

[93] R. K. Hobbie and B. J. Roth, Intermediate physics for medicine and biology. Springer, 2007.

[94] A. W. Wood, Physiology, Biophysics, and Biomedical Engineering. CRC Press, 2012.

[95] W. F. Boron and E. L. Boulpaep, Medical Physiology. Elsevier Health Sciences, 2008.

[96] C. Reid, “Spectroscopic Methods for Medical Diagnosis at Terahertz Wavelengths,” Ph.D. dissertation, Dept. of Med. Phys. Bioeng., Univ. College London, London, United Kingdom, 2009.

[97] S. J. Oh, J.-H. Son, O. Yoo, and D.-H. Lee, “Terahertz characteristics of electrolytes in aqueous Luria-bertani media,” J. Appl. Phy., vol. 102, 2007.

[98] Loyola University Chicago Stritch School of Medicine, “Histology Part6: Neural Tissue,” www.meddean.luc.edu. [Online]. Available: http://www.meddean.luc.edu /lumen/meded/Histo/frames/histo_frames.html [Accessed: May. 2, 2015]

[99] W. G. Yeo, V. Sanphuang, and N. K. Nahar, "THz periodic surfaces to enhance spectroscopic measurements," in ICEAA, 2012, pp. 924-925.

[100] V. Sanphuang, W. G. Yeo, J. L. Volakis, and N. K. Nahar, “THz Transparent Metamaterials for Enhanced Spectroscopic and Imaging Measurements,” IEEE Trans THz Sci. Technol., 5(1), pp. 117-123, Jan. 2015. 130

[101] R. A. Shelby, D. R. Smith, and S. Schultz, “Experimental verification of a negative index of refraction,” Science, vol. 292, no. 5514, pp. 77-79, Apr. 2001.

[102] V. G. Veselago, "The electrodynamics of substances with simultaneously negative values of ε and µ," Sov. Phy. Usp., vol. 10, pp. 509-514, 1968.

[103] S. A. Cummer, B. I. Popa, D. Schurig, D. R. Smith, and J. Pendry, “Full-wave simulations of electromagnetic cloaking structures,” Phys. Rev. E, vol. 74, p. 03662, Sep. 2006.

[104] D. Schurig, J. J. Mock, B. J. Justice, S. A. Cummer, and J. B. Pendry, “Metamaterial electromagnetic cloak at microwave frequencies,” Science, vol. 314, no. 5801, pp. 977-980, Nov. 2006.

[105] J. L. Volakis, G. Mumcu, K. Sertel, C. C. Chen, M. Lee, B. Kramer, D. Psychoudakis, and G. Kizitas, “Antenna miniaturization using magnetic-photonic and degenerate band edge crystals,” IEEE Antennas Propag. Mag., 48(5), pp. 12-18, Oct. 2006.

[106] C. M. Soukoulis and M. Wegener, “Past achievements and future challenges in the development of three-dimensional photonic metamaterials,” Nature Photon., vol. 5, pp. 523-530, Jul. 2011.

[107] J. W. Lee, M. A. Seo, J. Y. Sohn, Y. H. Ahn, and D. S. Kim, “Invisible plasmonic meta-materials through impedance matching to vacuum,” Opt. Express, 13(26), pp. 10681-10687, Dec. 2005.

[108] A. Srivastava, O. J. Kwon, and M. Sattorov, “Broadband THz vacuum window using impedance matching approach,” in IRMMW-THz, 2010, p. 1.

[109] J. Kröll, J. Darmo, and K. Unterrainer, “Metallic wave-impedance matching layers for broadband terahertz optical systems,” Opt. Express, 15(11), pp. 6552-6560, May. 2007. 131

[110] H. Odabasi and F. Teixeira, "Analysis of Canonical Low-Profile Radiators on Isoimpedance Metamaterial Substrates," Radio Sci., vol. 47, no. 1, Jan. 2012.

[111] H. Odabasi, F. L. Teixeira, and W. C. Chew, "Impedance-matched absorbers and optical pseudo black holes," J. Opt. Soc. Am. B, vol. 28, no. 5, pp. 1317-1323, May 2011.

[112] P. Planken, “Introduction to the Special Issue on ‘Terahertz Near-field Optics’,” J. Infrared Millim. Terahertz Waves, vol. 32, no. 8, pp. 975-975, Sep. 2011.

[113] J. B. Pendry, “Negative refraction makes a perfect lens,” Phys. Rev. Lett., vol. 85, no. 18, pp. 3966-3969, Oct. 2000.

[114] X. Zhang and Z. Liu, “Superlenses to overcome the diffraction limit,” Nat. Mater., vol. 7, pp. 435-441, Jun. 2008.

132