PLEXAR IMAGING: A STARTUP DETERMINED TO SOLVE THE CT DOSE VARIABILITY PROBLEM
by
SHISHIR RAJ ADHIKARI
Submitted in partial fulfillment of the requirements For the Degree of Master of Science
Department of Physics CASE WESTERN RESERVE UNIVERSITY
August 2013
CASE WESTERN RESERVE UNIVERSITY SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of Shishir R Adhikari candidate for the Master of Science degree*. (signed) Edward M. Caner (chair of the committee)
Bruce E. Terry Steven Izen Edward M. Caner
(date) 06/25/2013
*We also certify that written approval has been obtained for any proprietary material contained therein.
Dedicated to my Family.
Table of Contents
LIST OF FIGURES ...... 4
LIST OF TABLES ...... 6
ABSTRACT ...... 7
PLEXAR IMAGING ...... 8
NEED ...... 8
DOSE VARIABILITY ...... 9
LACK OF STANDARDS OR COMMON METRICS ...... 13
DEMAND FROM CT TECHNOLOGISTS ...... 14
CANCER ...... 16
INTRODUCTION TO CT ...... 18
CT IMAGES ...... 21
ATTENUATION COEFFICIENT AND HOUNSFIELD UNIT ...... 23
IMAGE QUALITY (IQ) ...... 24
IMAGE NOISE ...... 25
TUBE CURRENT ...... 26
SCAN (ROTATION) TIME ...... 26
SLICE THICKNESS ...... 27
PEAK KILOVOLTAGE (KVP) ...... 27
RECONSTRUCTION ALGORITHM ...... 28
PLEXAR’S TECHNOLOGY ...... 29
SCANNER CHARACTERIZATION ...... 30
1 GREEN LINE GENERATION ...... 34
PROTOCOL RECOMMENDATION ...... 37
MARKET ...... 38
NORTH AMERICAN MARKET SHARE FOR CT SCANNERS BY COMPANY ...... 40
MARKET SIZE ...... 42
COMPETITION/ALTERNATIVES ...... 43
GUIDANCE SYSTEM ...... 44
DUKE UNIVERSITY’S RESEARCH ...... 45
DOSE REDUCTION STRATEGIES ...... 46
Fixed Tube Current (Technique Chart) ...... 46
Tube Current (mA) Modulation ...... 47
Angular (x,y) mA Modulation ...... 48
Longitudinal (z) mA Modulation ...... 49
Angular and Longitudinal (x,y,z) mA Modulation ...... 50
Automatic Exposure Control (AEC) ...... 51
Iterative Reconstruction (IR) ...... 52
BUSINESS MODEL ...... 57
TRIAL PERIOD ...... 57
Freemium ...... 57
CONVERGECT ...... 60
Customer Solution Profit Model (CSP) ...... 60
Pay per Scan Model ...... 61
DOSE REDUCTION QUANTIFIER (DRQ) ...... 61
CT OEMs ...... 61 2 FDA ...... 61
SWOT ANALYSIS ...... 62
STRENGTHS ...... 62
WEAKNESSES ...... 64
OPPORTUNITIES ...... 64
THREATS ...... 65
SO STRATEGIES ...... 66
WO STRATEGIES ...... 66
ST STRATEGIES ...... 67
WT STRATEGIES ...... 67
FIVE FORCES ANALYSIS ...... 69
THREATS OF NEW ENTRANTS ...... 69
THREAT OF SUBSTITUTES ...... 69
BARGAINING POWER OF BUYERS ...... 70
BARGAINING POWER OF SUPPLIERS ...... 71
RIVALRY AMONG EXISTING COMPETITORS ...... 71
APPENDIX ...... 72
SIGNAL DETECTION THEORY ...... 72
Image ...... 72
Observer ...... 73
Why use channels? ...... 77
How are channels chosen? ...... 77
BIBLIOGRAPHY ...... 79
3 List of Figures
FIGURE 1: WORD FREQUENCY HISTORY VS TIME ...... 8
FIGURE 2: DIFFERENT VARIABLES THAT A CT TECHNOLOGIST NEEDS TO CONSIDER ...... 9
FIGURE 3: VARIABILITY IN RADIATION DOSE ACCORDING TO PATIENT SIZE ...... 10
FIGURE 4:DOSE VARIATION EVIDENT IN ABDOMEN AND PELVIS SCAN CONDUCTED AT DIFFERENT SCAN CENTERS. .. 12
FIGURE 5: PROJECTED NUMBER OF FUTURE CANCERS THAT COULD BE RELATED TO CT SCAN USE IN THE US IN 200718
FIGURE 6: ANATOMY OF CT SCAN ...... 20
FIGURE 7: A SIMPLE CT SYSTEM ...... 22
FIGURE 8: X-RAY PASSING THROUGH A BLOCK OF THICKNESS X ...... 23
FIGURE 9: THE HOUNSFIELD SCALE ...... 24
FIGURE 10: EFFECT OF NOISE: (A) LOW CONTRAST OBJECTS ARE VISIBLE (B) LOW CONTRAST OBJECTS ARE HARD TO
IDENTIFY ...... 26
FIGURE 11: (A) WATER PHANTOM SCANNED AT 80MAS (B) AND SAME PHANTOM SCANNED AT 40MAS ...... 27
FIGURE 12: EFFECT OF RECONSTRUCTION ALGORITHM ON THE IMAGE ...... 28
FIGURE 13: CONVERGECT PHANTOM ...... 30
FIGURE 14: CHO-SNR (D’) VS. FLUX INDEX GRAPH ...... 32
FIGURE 15: CONTRAST INDEX VS FLUX INDEX GRAPH ...... 33
FIGURE 16: GRAPHICAL REPRESENTATION OF SCANNER CHARACTERIZATION ...... 34
FIGURE 17: DOSE (CTDIVOL) VS. WATER EQUIVALENT DIAMETER GRAPH ...... 35
FIGURE 18: IMAGE QUALITY (IQ) VS. WATER EQUIVALENT DIAMETER GRAPH ...... 37
FIGURE 19: TOTAL GLOBAL MARKET SIZE AND GROWTH FOR THE CT SCANNERS MARKET, 2012-2017 ...... 39
FIGURE 20: TOTAL NORTH AMERICAN MARKET SHARE FOR CT SCANNERS, BY COMPANY 2012 ...... 40
FIGURE 21: BRAND LOYALTY DYNAMICS ...... 42
FIGURE 22: GRAPH (TOP) OF RELATIVE ATTENUATION VALUES AS A FUNCTION OF TABLE POSITION AND ASSOCIATED
BODY REGION (BOTTOM) SHOWS ALMOST THREE ORDERS OF MAGNITUDE OF VARIATION IN ATTENUATION,
ACCORDING TO BODY REGION AND PROJECTION ANGLE...... 48
4 FIGURE 23:GRAPH OF RELATIVE TUBE CURRENT SUPERIMPOSED ON A CT PROJECTION RADIOGRAPH ILLUSTRATES THE
CONCEPT OF LONGITUDINAL DOSE MODULATION...... 50
FIGURE 24:DIFFERENCE BETWEEN FBP AND IR ...... 53
FIGURE 25: DIFFERENCE BETWEEN FBP AND VEO ...... 54
FIGURE 26: IMAGE OBTAINED WITH FBP RECONSTRUCTION (LEFT) AND IRIS RECONSTRUCTION (RIGHT) ...... 55
FIGURE 27: IMAGE OBTAINED WITH FBP RECONSTRUCTION (LEFT) AND AIDR 3D RECONSTRUCTION (RIGHT) ...... 56
FIGURE 28: IMAGE OBTAINED WITH FBP RECONSTRUCTION (LEFT) AND IDOSE4 RECONSTRUCTION (RIGHT) ...... 57
FIGURE 29: FREEMIUM AND PREMIUM BUSINESS MODEL ...... 58
FIGURE 30: WORKINGS OF FREE DOSE ASSESSMENT SOFTWARE ...... 59
FIGURE 31: FREEMIUM TO PREMIUM ...... 59
FIGURE 32: CUSTOMER SOLUTION PROFIT ...... 60
FIGURE 33: DIAGRAMMATIC REPRESENTATION OF CHO ...... 75
5 List of Tables
TABLE 1: DRAMATIC DOSE VARIATION BETWEEN FACILITIES: FOR THE SAME CT STUDY TYPES ...... 12
TABLE 2: CTDIVOL RELATIONSHIP CHART ...... 14
TABLE 3:TRADEOFFS BETWEEN RADIATION DOSE AND IMAGE QUALITY ...... 29
TABLE 4: PIN DIAMETERS AND CONTRAST VALUES IN DIFFERENT PHANTOM SECTION ...... 31
TABLE 5: TOTAL GLOBAL MARKET SIZE AND GROWTH FOR THE CT SCANNERS MARKET, THROUGH 2017 ...... 39
TABLE 6: TOTAL NORTH AMERICAN MARKET SHARE FOR CT SCANNERS, BY COMPANY 2012 ...... 40
TABLE 7: COMPETITION LANDSCAPE ...... 43
TABLE 8: DIFFERENT AEC ALGORITHM USED BY DIFFERENT CT VENDORS ...... 52
TABLE 9: SWOT ANALYSIS ...... 68
6 Plexar Imaging: A Startup Determined to Solve the CT Dose Variability Problem
Abstract by
SHISHIR RAJ ADHIKARI
Computed Tomography (CT) is a very popular diagnostic imaging technique.
Almost 85.3 million scans were done in 2011. A CT scan uses about 100-500 times more ionizing radiation than a single X-ray image. However, the lack of clear dose reference levels, and lack of professional or governmental organizations for collecting and reporting dose data have exacerbated high dose variability. Moreover, studies have shown correlation between CT scans and the chance of getting cancer.
Plexar Imaging, a startup based in Cleveland, has found an innovative way to reduce the dose variability. Plexar’s software can suggest the minimum dose required to produce an acceptable image. This paper discusses Plexar’s technology, analyzes the
CT scans market, draws out business strategies, and suggests a viable business model for Plexar Imaging.
7 Plexar Imaging
Plexar Imaging (PI) has developed ConvergeCT*, a unique, proprietary, patent- pending solution that automatically computes the optimal dose setting for the CT radiation technologist. By eliminating the guesswork, ConvergeCT drastically reduces dose variability in the clinical setting; under-dose scans are brought up to diagnostic image quality, unnecessarily high doses are reduced, and, consequently, average patient dose is significantly reduced.
Need
If we look at the “word frequency history”† (Fig. 1), we see that from the year 1800 there is an exponential growth in the use of “need.” 1800 marks the beginning of the industrial revolution and the exponential growth of the use of “need” suggests that there is strong correlation between the development of technology and use of
“need”. Thus, it is logical to start an analysis of a technology by analyzing the need for it.
Figure 1: Word Frequency History vs Time1
* ConvergeCT is PI principal product and other products can be derived from it. Other products may be Green Line Generation software, Dose Reduction Quantifier software for OEMs. † Based on a Google Books sample of one million books in English. “Quantitative Analysis of Culture Using Millions of Digitized Books.” 8
Dose Variability
A CT technologist needs to choose among many variables to scan a patient.
As illustrated in Fig. 2, the complexity clouds can easily create confusion as to
what a correct dose should be. Thus, choosing a correct dose is a complex
process.
Figure 2: Different Variables that a CT Technologist Needs to Consider
Figure 3 shows a hypothetical illustration of dose variability for a range of
patient sizes. Patient size can be read from the x-axis and the corresponding
dose from the y-axis. For example, it is evident from the graph that there is
an almost 2X variance in the dose for a patient size of 300mm, which means
one patient of size 300mm is getting two times more dose than another
9 patient of equivalent size. This unnecessary variability in dose is completely
unacceptable.
Figure 3: Variability in Radiation Dose According to Patient Size
Why are Doses so Variable?2
Doses are variable for following reasons:
1. Except in California*, there is no legal obligation for collecting and
reporting dose data. Consequently, there are no penalties for
improper quality control.
* California passed CT Radiation Dose reporting law (http://www.leginfo.ca.gov/pub/09- 10/bill/sen/sb_1201-1250/sb_1237_bill_20100929_chaptered.html) 10 2. According to the FDA, doses should be As Low As Reasonably
Achievable (ALARA) but there are no guidelines for what doses are
reasonable or achievable.
3. Technologists who conduct CT examinations receive no consistent
education on what doses are excessive. Thus, technologists may
end up making mistakes.
In Fig. 4 the x-axis represents patient’s weight and the y-axis represents dose
length product (DLP)*. Fig. 4 clearly shows almost 50% of doses are above
the maximum radiation threshold (Red line) set by American College of
Radiologist (ACR).
* DLP is CTDIvol times scan length. To better represent the overall energy delivered by a given scan protocol, the CTDIvol can be integrated along the scan length to compute Dose Length Product (DLP). 11
Figure 4:Dose Variation Evident in Abdomen and Pelvis Scan Conducted at different Scan Centers.3 The doses collected for the same protocol from four CT scan centers are
presented in the Table 1. Table 1 clearly shows that there is huge variation in
the doses. Thus, proving that dose variability is rampant.
Table 1: Dramatic Dose Variation Between Facilities: for the Same CT Study Types4
Anatomic Area and Site 1 Site 2 Site 3 Site 4 Variation Indication for CT Dose Dose Dose Dose (mSv) (mSv) (mSv) (mSv) Head Routine Head 3 2 3 2 20X’s Suspected Stroke 18 15 8 29 14X’s Chest Routine Chest 5 12 11 7 12X’s Suspected PE 8 21 9 9 15X’s Abdomen Routine 12 19 20 12 14X’s Multiphase 24 35 45 34 15X’s
12 Lack of Standards or Common Metrics
There is no commonly accepted metric or standard for dose reduction. There
are many ways to measure dose (for example: CTDIvol, Dose Length Product,
Effective Dose, etc.). Some people prefer one to the other ways to measure
dose. Generally, the electric current running through the x-ray tube sets dose.
Unfortunately, different tubes will produce different radiation given the same
current, because of different filters, age of tube, etc. Thus, dose is not a good
metric. This is where PI changes the playing field. By providing a measured
scale based on IQ, a standard can be set.
Once different images from different machines are obtained, each of those
images can be categorized on the basis of image quality (such as image noise
and low contrast detectability). Thus, image quality can be set as a constant
(independent variable) for different scanners to determine dose. This way,
Plexar’s technology establishes image quality as a universal metric. Plexar is
thus not only focused on reducing dose but also focused on developing image
quality as a universal metric to accomplish the task of reducing the
unnecessary variation objectively and quantifiably.
13 5 Table 2: CTDIvol Relationship Chart
Demand from CT Technologists6
IMV* survey respondents were asked the following question about their
perspectives on their department priorities: “Over the next year, which of the
following factors will be a priority to the mission of your CT department?”
The respondents rated (using a 5-point importance scale, where 1 =
“relatively unimportant” and 5 = “very important”) “Improve the capacity to
reduce radiation dose to patients” as the most important factor. Thus, dose
reduction is seen as one of the most important problems that need to be
addressed.
* The IMV 2012 CT Market Outlook Report is based on the responses from 400+ hospital and imaging center radiology professionals across the United States. 14 When respondents were asked to provide suggestions for product capabilities or service they would like to see provided by CT vendors: over two thirds of the comments encompassed: “Dose Reduction Improvements.”
Suggestions under Dose Reduction Improvements include:
1. Would like to see GE provide radiation reduction software for 16 slice
scanners.
2. Dose reduction for older scanners.
3. Tracking of radiation dose for patients and technologists.
4. Summary of dose page.
5. Standardization of Patient Dose reporting.
6. Software upgrade to reduce and track radiation dose.
7. Simplification of dose reduction, and real-time dose monitoring.
8. Real time dose calculations, accumulative if possible for repeat studies
by patient.
9. A way to track radiation dose from visit to visit for patients who have
multiple studies and show a cumulative total.
10. More focus on automatic dose calculations.
11. Measuring and documenting radiation dose.
12. Lower dose even further.
13. Keep working on dose reduction. Dose data that can be sent to PACS
and saved.
14. Iterative reconstruction/dose reduction software capabilities
standard on all scanners. 15 15. Easier to tell what dose patients are getting and how to decrease.
16. Dose reduction software should be a standard upgrade to no cost to
the facility. The patient benefits from the reduced exposure to
radiation not the manufacturer and not the hospital.
17. Continuing development of dose-reduction functions while
maintaining diagnostic quality scans.
18. Any product making dose monitoring easier, prevention of overdose,
and dose integration with radiology report.
Cancer7
As discussed earlier, a CT scan uses x-rays. So an obvious question to ask is :
How do x-rays increase the risk of cancer?
When x-rays, or any ionizing radiation, pass through the body they
cause electrons to be ejected from atoms, leaving behind positive ions.
These positive ions, or free radicals, can cause damage to DNA. DNA
can also be damaged directly by radiation. If DNA is damaged, there
are three possible outcomes:
1. The cell dies (only occurs with very high dose).
2. The cell repairs itself perfectly (most common result).
3. The cell repairs itself with mistakes (rare).
16 The inaccurate repair of DNA is rare, but can cause a cell to act wildly or grow into cancer. Oftentimes it takes decades for cancer to be detected following radiation exposure.
However, there are not trustworthy scientific studies directly linking CT scan and cancer because to scientifically prove a connection would require nearly one million patients followed closely over decades to detect the small increased risk with any confidence.
The potential cancer risk from CT use has been estimated using risk projection models derived primarily from studies of survivors of the atomic bomb explosions in Japan. The lifetime risks of cancer due to CT scans, which have been estimated using risk projection models based on atomic bomb survivors, are about one case of cancer of every 1,000 people who are scanned, with a maximum incidence of about one case of cancer for every
500 who are scanned.8
Natural background radiation exposure accounts for an average of 3.1 mSv/yr with variations depending on where people live.9 In the US, the average person is exposed to an additional 3.0 mSv/yr from medical sources
(predominantly CT scans).10 The average US total radiation exposure is 6.2 mSv/yr which is an increase from 20 years ago (3.6 mSV/yrs), when CT scans were less common.11
17 Even though there are not any real scientific studies completed on the
causation relation between CT scan and cancer, there has been a Monte Carlo
simulation conducted to project cancer risks from CT scans performed in the
US. That simulation estimated approximately 29,000 future cancers could be
related to just the CT scans performed in the US, in 2007.12
Figure 5: Projected number of future cancers that could be related to CT scan use in the US in 200713 Introduction to CT
Before presenting PI’s technology that solves the aforementioned problems, it’s beneficial to comprehend basics of a CT scan. Conventional X-ray imaging produces a two-dimensional image of a three-dimensional object, which leads to overlapping of structures, although they are completely separate in the object. This is overlapping is particularly troublesome in medical diagnosis in which there are
18 many anatomic structures that can interfere with what the physician is trying to see.
This problem was solved in the early 1970s with the introduction of a technique called Computed Tomography (CT).
CT is a diagnostic imaging technique, in which images are produced by rapid rotation of the X-ray tube around the patient. The transmitted radiation is then measured by a ring of sensitive detectors located on a gantry around the patient (Fig
6). A series of cross-sectional X-ray images, also called projections, are acquired and processed using the computer to produce two- dimensional images of a three- dimensional object across various axes of the body. Sometimes three-dimensional volumetric images are produced but those images are not subject of this thesis.
19
Figure 6: Anatomy of CT Scan14
The common uses of CT are chest, abdominal and head imaging. Unlike conventional
X-ray, CT allows differentiation of soft tissue structures such as liver and fat, making it suitable for a range of applications from oncology to cardiovascular; and many other advanced applications such as pediatric imaging and coronary CT angiography.
Due to short scan times on the order of 500 milliseconds to a few seconds, CT can be used for all anatomic regions, including those susceptible to patient motion and breathing. CT imaging of the head and brain can detect tumors, blood clots, and blood vessel defects as well as other abnormalities such as enlarged ventricles caused by the accumulation of cerebrospinal fluid and those of the nerves or muscles of the eye.
20 CT is also gaining ground rapidly in interventional procedures such as guided biopsy and minimally invasive therapy. CT images are used in planning radiotherapy cancer treatment and imaging of bony structures, complex joints and spines in orthopedics.
CT exams, with relatively fast exam times and the high level of detailed anatomic information, are becoming the method of choice in emergency and trauma cases, which require quick decision-making.
CT Images
Figure 7 illustrates a simple geometry for acquiring a CT slice through the center of the head. A narrow pencil beam of x-rays is passed from the x-ray source to the x- ray detector. The measured value at the detector is related to the total amount of material placed in between the source and the detector. A quantity that characterizes how easily a material can be penetrated by a beam of x-ray is called the linear attenuation coefficient* (µ). Materials such as bone and teeth block more of the x-rays, resulting in a lower signal compared to soft tissue and fat. Thus, bone and teeth have a larger µ than soft tissue and fat.
* More discussion on attenuation coefficient on section () 21
Figure 7: A simple CT system 15
As shown in Fig. 7, the source and the detector assemblies are translated to capture the whole cross section. In Fig. 7 only a linear translation and only one view is shown. However, for a complete CT scan the source and the detector are mounted on a rotating gantry that surrounds the patient. A key feature of CT data acquisition is that x-rays pass only through the slice of the body being examined.
The transmission of x-rays through the patient for a monoenergetic beam is given by:16
−µx I = I0e
where I is the intensity of the x-ray measured by the detector, I0 is the intensity of the x-ray from the source, x is the thickness
22
Figure 8: X-ray passing through a block of thickness x
If the x-ray beam is intercepted by two regions with attenuation coefficient μ1 and μ2
17 and thicknesses x1 and x2, the x-ray transmission is given by
−(µ1x1+µ2x2 ) I = I0e
If the attenuation coefficient is regarded as a function of position, then the x-ray transmission is given by:18
xn − ∫ µ(x)dx xo I = I0e
xn I µ =−ln ∫ I x0 0
With multiple transmission measurements at different orientations of the x-ray source and detector, the line integrals of the attenuation coefficients over all lines through the object can be obtained. Then using inverse Radon transform and image reconstruction algorithm, a CT image is obtained.
Attenuation Coefficient and Hounsfield Unit19
Every substance through which an x-ray passes has the property that each millimeter of the substance absorbs a certain proportion of the photons that pass 23 through it. This proportion, which is specific to the substance, is called the attenuation coefficient of that material. In general the attenuation coefficient is nonnegative and its value depends on the substance involved. Bone has a very high attenuation coefficient, and water is somewhere in between.
Radiologists actually use a variant of the attenuation coefficient in their work.
Hounsfield Unit (CT number/HU) associated with a medium is a number that represents a comparison of the attenuation coefficient of the medium with that of water. Specifically, the HU of a medium is
µmedium − µwater H medium = ×1000 µwater
The Fig. 9 shows the HU scale and value of HU for different materials:
Figure 9: The Hounsfield Scale20 Image Quality (IQ)21
CT IQ can be described in terms of image contrast, spatial resolution, image noise, and artifacts. IQ revolves around the ability to accurately depict anatomy within the image. Two main features help to assess how well the image represents real anatomy: Spatial Resolution and Low Contrast Detectability. Spatial resolution is the
24 ability to distinguish small, closely spaced objects on an image. For example, if two thin wires lie close together in an object, will they be seen as two separate lines on the image? Low contrast detectability is the ability of the system to differentiate between objects with similar densities. For example, will an object that is nearly the same density as its background be distinguishable on the CT image?
A strength of CT is its ability to visualize structures of low contrast in a subject, a task that is limited primarily by noise and is therefore closely associated with radiation dose: the higher the dose contributing to the image, the less apparent the image noise and the easier it is to perceive low-contrast structures.
Image Noise22
When a graphic cursor is moved over an image of a uniform phantom (e.g. water phantom), it is seen that the CT numbers are not uniform but fluctuate around an average value (which should be approximately zero for water). These random fluctuations in the CT number of otherwise uniform material appear as graininess on CT images. This graininess is due to the use of limited number of photons to form the image. Thus, CT image noise is associated with the number of photons contributing to each detector measurement. To comprehend how CT technique affects noise, one should understand how each factor in the technique affects the number of detected photons.
25
Figure 10: Effect of Noise: (a) Low contrast objects are visible (b) low contrast objects are hard to identify23
Tube Current24
Changing the tube current (mA) changes the x-ray beam intensity and thus
the number of photons. For example, doubling the mA will double the beam
intensity and the number of photons detected by each measurement.
Scan (rotation) Time25
Changing the scan time changes the duration of each measurement and thus
the number of detected photons. Because tube current and scan time
similarly affect noise and patient dose, they are usually considered together
as mA X s, or mAs. This is illustrated in the water phantom images (Fig. 11)
below in which the image on the left was scanned at 80mAs, while the image
on the right was scanned at 40mAs.
26
Figure 11: (a) Water phantom scanned at 80mAs (b) and same phantom scanned at 40mAs26 Slice Thickness27
Changing the thickness changes the beam width entering each detector and
thus the number of detected photons. For example, compared with a slice
thickness of 5mm, a thickness of 10mm approximately doubles the number
of x-rays entering each detector.
Peak Kilovoltage (kVp)28
kVp effects noise and attenuation. Increasing the kVp changes the number of
x-rays penetrating the patient and reaching the detectors. Thus increasing
the kVp may reduce image noise and change subject contrast as well.
27 Reconstruction Algorithm29
CT images are acquired by taking a series of x-ray projections around the
subject at various angles. This data can be used to reconstruct a slice of
subject by compiling the images taken at different angles of the rotation. The
algorithm used to reconstruct an image from the projection data is called
reconstruction algorithm. The reconstruction algorithm can have a
substantial effect on the standard deviation*. For example in Fig. 12,
Algorithm 1 has a standard deviation of 3.1 and Algorithm 2 has a standard
deviation of 12.5 even though both were reconstructed from the same data.
Figure 12: Effect of reconstruction algorithm on the image30
* Image noise, in its most simple definition, is measured as the standard deviation of voxel values in a homogenous (typically water phantom). 28 Table 3:Tradeoffs between Radiation Dose and Image Quality31
Reducing mAs Increase Table Speed or Pitch 1. Reduces radiation dose in 1. Reduces radiation dose in proportion to reduction in mAs proportion to increase in pitch 2. But increases image noise in (UNLESS the scanner proportion to automatically increases mA with an increase in pitch as Siemens and Philips, multidetector CT mAsoriginal scanners do) mAsreduced 2. But, may accordingly reduce resolution Reducing kVp* 1. Reduces radiation dose (though not linearly with kVp) 2. May increase or decrease artifacts
Plexar’s Technology
Plexar’s product is called ConvergeCT. ConvergeCT consists of a software package and a phantom. To implement ConvergeCT, a CT scanner is calibrated using
ConvergeCT Phantom. The calibration of the CT scanner helps to develop a scanner characterization curve. Using a set of retrospective data for a selected diagnostic task, Plexar’s customers generate a target optimal image quality function (The
Green Line). Finally, using characterization curve and the green line curve, mAs is recommended to get a desired optimum image quality. The detailed workings of the
ConvergeCT are divided into three sections:
* Beam Energy: energy of x-ray beam 29 Scanner Characterization
For scanner characterization, Plexar uses a ConvergeCT Phantom (Fig. 13).
The ConvergeCT phantom, unique and proprietary, has multiple diameters
(12-45* cm) and simulates x-ray attenuation for a wide range of patient sizes.
It is designed to enable complete flux index† (eq. 2) range coverage of any
make or model scanner.
Figure 13: ConvergeCT Phantom For each diameter, the phantom includes a uniform section, a pin section, and
a wedge section. Each diameter has three different sections: a) Uniform
section, with uniform contrast, b) Pin section and c) Wedge section, multiple
contrasts. Pin sections have six different pins as shown in Table 4. Wedge
sections have five wedge regions with contrast values of 2,4,8,16, and 32 HU.
* 45 cm phantom is not used till now. † Flux index is a measure of the flux available on the detector. 30
Table 4: Pin Diameters and Contrast Values in Different Phantom Section
Section Pin Diameter 2 mm 5 mm Contrast in HU Small (13.6 cm) 2, 4, 8 2, 4, 8 Medium (21.2 cm) 4, 8, 16 4, 8, 16 Large (30.9 cm) 8, 16, 32 8, 16, 32 XLarge (45 cm) 32, 64, 128 32, 64, 128
Flux index is defined as: FluxIndex = mAs × Slicethickness × e−(objDiam−refDiam)×µwater (2)
By selecting several protocols with different mA settings, scan times, and
slice thickness settings, flux index can be varied. The flux index for each
phantom image is computed using Eq. 2. At each protocol, the phantom is
scanned repeatedly to get hundreds of images. Currently, a Channelized
Hotelling Observer (CHO)* algorithm is used for the detectability
computation. The detectability of the pins is computed. That detectability is
denoted by CHO-SNR(d’)†. The d’ vs. flux index is plotted as shown in Fig. 14.
* Discussed in detail in Appendix (Signal Detection Theory) † There are other ways to calculate SNR. Plexar has used both NPWE and CHO to get SNR. For the purpose of understanding the technology CHO-SNR is sufficient. 31
Figure 14: CHO-SNR (d’) vs. Flux Index Graph*
The CHO-SNR is normalized to get an image quality metric called Contrast
Index. A graph between Contrast index vs. flux index graph (Fig. 15). This
curve is called a scanner characterization curve.
* Each color represent following contrast value (in HU): Brown 2, Turquoise 4, Green 8, Purple 16, and Pink 32. Shapes represent different diameters: Triangle small, Asterisk medium, Plus large. 32
Figure 15: Contrast Index vs. Flux Index Graph
33
Figure 16: Graphical Representation of Scanner Characterization Green Line Generation
A set of retrospective data for a selected diagnostic task is collected and used
by PI’s customers to generate a target optimal image quality function (The
Green Line). For all CT images, the CTDIvol (Dose) can be extracted from the
34 DICOM header or from the dose report, and the Water Equivalent (WEQ)
Diameter is calculated*. Dose vs. WEQ diameter is plotted as shown in Fig. 17.
† Figure 17: Dose (CTDIvol) vs. Water Equivalent Diameter Graph
* Discussed in Protocol recommendation section. † This is a hypothetical example. 35 The technology has two-fold goals:
1) Enable customers to identify dose variability
2) Bring together all scattered data points (unnecessary variability)
closer to the green line (consistent image quality) in prospective
scans
Since this green line gets used in the clinical setting, radiologists build the green lines based on acceptable image quality. Plexar is developing additional tools to speed the radiologists’ protocol development process.
Using a particular scanner characteristic curve and Dose vs. Water
Equivalent Diameter graph, Plexar’s software creates an Image Quality
(Contrast Index) Vs. Water Equivalent Diameter graph (Fig. 18). In this graph only one green line is seen for two different cases. From Fig. 18, Plexar has hypothesized and has based its business on the proposition that image quality can be established as a universal metric and should be used to guide protocol selection.
36
Figure 18: Image Quality (IQ) vs. Water Equivalent Diameter Graph Protocol Recommendation
In this section, the result from the characteristic curve and the IQ vs. Water
Equivalent Diameter graph is used to recommend mAs to get a desired
optimum Image Quality.
Water Equivalent Diameter can be calculated using the pre-scan image (scout
image):
I(x, y) D = 2 × (3) weq ∑ π
37 where I(x, y) is obtained from the scout image:
⎛ image(x, y) ⎞ I(x, y) = +1 × Pixel Area (4) ⎝⎜ 100 ⎠⎟
From the green line on Fig. 18 and the calculated Water Equivalent Diameter
the needed IQ can be obtained for that patient size. Using the scanner
characterization curve (Fig. 15) the necessary flux index is computed. From
the flux index, slice thickness, water equivalent diameter and using equation
2, the recommended mAs is obtained.
Market
The global market for computed tomography scanners was valued at $3.7 billion in
2012.32 Driven by high-slice systems, technology innovation, and the increasing disease coverage capabilities of CT over time, the market is forecast to grow at a compound annual growth rate (CAGR) of 6.7% to reach $5.1 billion by 2017.33 The global CT scanners market is further fueled by the technological advances of high- slice systems over their mid- and low-slice counterparts, use of high-slice scanners in cardiology and oncology, and the high CAGRs exhibited by Asian and emerging markets.
As the markets with the largest installed base of CT scanners, the U.S., Europe, and
Japan being to mature; the focus of manufacturers will shift from adding features to optimizing existing technologies to meet market needs. Since Plexar is developing software and a phantom system to optimize existing technologies, Plexar is on the right track to address the CT scanning market.
38
Table 5: Total Global Market Size And Growth for the CT Scanners Market, Through 201734
Region 2011 2012 2017 CAGR% (2012- ($ Millions) ($ Millions) ($ Millions) 2017) Asia 1,250.0 1,350.0 2,150.0 9.8 Europe 1,000.0 1,050.0 1260.0 3.7 North America 800.0 850.0 1,050,0 4.3 Emerging market countries 395.0 425.0 625.0 8.0 Total 3,445.0 3,675.0 5,085.0 6.7
Figure 19: Total Global Market Size And Growth for the CT Scanners Market, 2012-201735
The market is divided into the following regions: North America (U.S., Canada),
Europe (France, Germany, Italy, Spain, U.K.), Asia (Japan, China, India), and
Emerging Markets (Australia, Brazil, Israel, Russia).
39 North American Market Share for CT Scanners by Company36
As evident from Table 6 and Fig. 20, GE, Siemens, and Toshiba held the large
share of North American CT scanners market in 2012. Together, these
companies captured 78% of the total market revenues.
Table 6: Total North American Market Share For CT Scanners, by Company 201237
Figure 20: Total North American Market Share For CT Scanners, by Company 201238
Figure 21 shows IMV’s brand loyalty survey result. According that result:39
1. GE Sites: GE sites that are in the purchase mode are relatively loyal to
GE with 72% of the sites considering GE again, followed by 34%
40 considering Siemens, 28% considering Toshiba, and 23% considering
Philips.
2. Siemens Sites: Siemens CT users are also loyal to the Siemens brand,
with 77% indicating they are considering Siemens for their next CT,
followed by 42% considering GE, 21% Toshiba, and 23% considering
Philips.
3. Philips Sites: Philips CT users are more likely to consider GE for their
next purchase (73%), compared to 45% considering Philips, 34%
considering Toshiba, 32% considering Siemens, and 4% considering
Hitachi.
4. Toshiba Sites: 69% of Toshiba users are likely to consider Toshiba for
their next CT scanner, while 42% will consider GE, 39% Siemens, 3%
Philips, and 3% Hitachi.
Brand loyalty dynamics indicates that Philips might lose customers, thus
Plexar should work on GE’s, Siemens’or Toshiba’s system, so that they can
start conversation with one of these companies. Establishing working
relation with any of these companies will help Plexar to grab significant
market share.
41
Figure 21: Brand Loyalty Dynamics Market Size
In 2011, the total number of CT scans performed in the USA was about 85
million, in Japan was about 63 million, and in the UK was about 3 million. The
total of these results in about 151 million CT scans. This result is use in
Business model section to calculate the revenue.
42 Competition/Alternatives
The Table 7 shows the different competition/alternatives that Plexar has. For
reasons noted in the Table 7, Plexar’s technology seems to be the best solution out
there.
Table 7: Competition Landscape
Universal Dose Works Personalized Customized Tracks Image Variability for All for Patient for Performance Quality Reduction Scanner Size Physical over Time Metric Makes Scanner and Models ConvergeCT Yes Yes Yes Yes Yes Yes Automatic CT Vendor No No No Partial No No Dose Reduction (e.g. ASIR) Image-Based No No Yes Partial No No Dose Reduction (e.g. MedicVision) Auto No Partial No Yes No No Exposure Control Dose Check No Only No No No No extreme outliers GE Dose No Manual Yes No No No Watch Guidance No Yes Yes Yes Yes Yes System
43 Guidance System40
Guidance System is a CT radiation dose optimization system developed by
Dr. David Larson of Cincinnati Children’s Hospital Medical Center. From
Table. 7, the Guidance System seems to be the strongest competitors to
ConvergeCT.
The Guidance System provides a solution to the dose variability problem.
However, it considers only (Pixel Standard Deviation) image noise as a
measure of the image quality. Noise is an important component of IQ but not
the only component. Therefore, it is not the best system to reduce dose
variability. Even though, it is not the optimal system to reduce dose
variability, it is still a good system for CT systems using FBP reconstruction
algorithm.
Furthermore, David Larson has shown that the Guidance System works well
on CT systems with iterative reconstruction algorithms. Images constructed
by iterative reconstruction have very low noise. Since the Guidance System is
based on image noise, it can't reduce dose variability on iterative
reconstruction systems.
David Larson still has no product yet. Moreover, the Guidance System is
licensed to Radimetrics, which was bought by Bayer. The Guidance System
44 might be infringing on Plexar’s IP. Thus, even if the Guidance System materializes as a product, Plexar might have the upper hand.
Duke University’s Research
Duke’s radiology group has published several papers on dose variability studies, CT image quality, and dose reduction. However, there are no obvious or published patent applications; or no product announcements from Duke yet.
Last year Duke’s radiology group published a paper titled “Automated size- specific CT dose monitoring program: Assessing variability in CT dose.” In this paper they disclosed sophisticated software that shows dose variability.
However, the software does not offer a solution to the dose variability problem. Thus, Duke's current software is not comparable to ConvergeCT.
However, Duke's research history track shows that they could be on a track to develop a solution for the dose variability problem. Thus, Duke has a huge potential to be a strong competitor or a partner.
45 Dose Reduction Strategies
Different dose reduction strategies, which are alternatives/competitions to
PI are discussed below:
Fixed Tube Current (Technique Chart)41
Under normal circumstances, a CT image does not look “over-exposed”
in the sense being too dark or too light; the normalized nature of CT
data (i.e. CT numbers represent a fixed amount of attenuation relative
to water) ensures that the image appears properly exposed. As a
result, CT technologists are not technically compelled to decrease the
mAs for small patients, which may result in excess radiation dose for
these patients. It is, however, a fundamental responsibility of the CT
technologist to take patient size into account when selecting the
parameters that affect radiation dose, the most basic of which is the
mAs.
The CT technologist should be provided with appropriate guidelines
for mAs selection as function of patient size. These are often referred
to as technique charts. In CT, the mAs and tube potential (kVp) can be
all altered to give the appropriate exposure to the patient. However,
most commonly the kVp and gantry rotation time are standardized for
a given clinical application. The fastest rotation time is typically used
to minimize motion blurring and artifacts, and the lowest kVp
46 consistent with the patient size is selected to maximize image contrast.
Hence tube current is the primary parameter that is adapted to
patient size.
Tube Current (mA) Modulation42
Extremely large variations in patient absorption occur both with the
projection angle and the anatomic region, and are not considered
when using a fixed mA setting (Fig. 22). The projection with the most
noise primarily determines the noise of the final image. Hence, data
acquired of body parts having less attenuation can be acquired with
substantially less radiation without negatively affecting the final
image noise.
47
Figure 22: Graph (top) of relative attenuation values as a function of table position and associated body region (bottom) shows almost three orders of magnitude of variation in attenuation, according to body region and projection angle.
Angular (x,y) mA Modulation43
Angular (x,y) mA modulation addresses the variation in x-ray
attenuation around the patient by varying the mA as the x-ray tube
rotates about the patient. The technologist chooses the initial mA
value, and the mA is modulated upward or downward from the initial
value with a period of one gantry rotation. As the x-ray tube rotates,
48 the mA can be varied according to the attenuation information from
the scout image (Pre-scan image), or in near real-time according to
the measured attenuation from the 180° of the previous projection.
Longitudinal (z) mA Modulation44
Longitudinal (z) mA modulation addresses the varying attenuation of
the patient among anatomic regions by varying the mA along the
longitudinal z-axis of the patient, as shown in Fig. 23. Thus, the
technologist must provide as input to the algorithm the desired level
of image quality.
49
Figure 23:Graph of relative tube current superimposed on a CT projection radiograph illustrates the concept of longitudinal dose modulation. Angular and Longitudinal (x,y,z) mA Modulation45
Angular and longitudinal (x,y,z) mA modulation combines the
previous two method to vary the mA during rotation along the z-axis
of the patient. The technologist must still indicate the desired level of
image quality by one of the following methods. This is the most
comprehensive approach to CT dose reduction because the x-ray dose
is adjusted according to the patient attenuation in all three
dimensions.
50 Automatic Exposure Control (AEC)46
It is technologically possible for CT systems to adjust the x-ray tube
current in real-time in response to variations in x-ray intensity at the
detector. The method of adapting the tube current to patient
attenuation is known generically as AEC. AEC have demonstrated
reductions in dose of about 20-40%. An exception to this trend occurs
with obese patients. AEC is a broad term that encompasses not only
tube current modulation (to adapt changes in patient attenuation),
but also determining and delivering the “right” dose for any patient
(infant to obese) in order to achieve the diagnostic task.
These AEC systems might work together with PI’s system. So, these
systems compliment PI’s system as well as act as alternatives.
51 Table 8: Different AEC Algorithm Used by Different CT Vendors
Manufacturer AEC Trade Image Quality Goal Name Reference
General Auto mA, Noise Index Constant image noise Electric Smart mA regardless of attenuation level, Toshiba SureExposure Target Image Quality using tube currents within Level prescribed min and max values Siemens CARE Dose4D Quality Reference Constant image quality Effective mAs regardless of attenuation level, with reference to a target mAs level for a standard-sized patient. Philips DoseRight Reference Image Keep the same image quality as in the reference image, regardless of attenuation level.
Iterative Reconstruction (IR)
Filtered Back Projection (FBP) technology has been around since the
beginning of CT in the 70s. When using FBP, a trade-off between
spatial resolution and image noise has to be considered.47 While FBP
is robust and has fast reconstruction speed, filters accentuate noise in
the image. FBP takes only a single pass through the raw data, which
accounts for its fast speed.
IR allows decoupling of spatial resolution and image noise.48 The
image produced may have enhanced spatial resolution and reduced
image in low contrast areas. However, some clinicians think that
iteratively reconstructed images have a waxy or plastic look.49
52
Figure 24:Difference between FBP and IR In last 20 years, a variety of IR approaches have been developed. Due
to slow image reconstruction speed and, consequently, the demand
for extensive computer power, IR has not gained traction in the CT
arena.50 However, CT manufacturers are trying to develop IR that is
faster and produces image with low noise at low dose:
GE: ASiR and Veo51
GE introduced its Adaptive Statistical Iterative Reconstruction
(ASiR) program in 2008. Veo is the first MBIR (Model Based
Image Reconstruction) technology, and it received FDA
clearance in September 2011.
Veo is an extension of ASiR. Veo creates an initial image and
compares that back to the measure of raw data of the system. It
53 uses a model of the entire CT scanner: the tube, the detector,
the focal spot of the tube, and the slice thickness, and iterates
to create an optimized image of that patient.
Figure 25: Difference between FBP and Veo Siemens Healthcare: IRIS52
Iterative Reconstruction in Image Space (IRIS) is Siemen’s
approach to reconstruction, approved by the FDA in 2009. IRIS
translates the iterative loop into image domain, thus avoid the
long reconstruction time.
54
Figure 26: Image obtained with FBP reconstruction (Left) and IRIS reconstruction (Right) Toshiba: AIDR 3D53
AIDR 3D is Toshiba’s third generation iterative dose reduction
software that uses algorithm designed to work in both the raw
data and image data space. Toshiba claims that AIDR 3D not
only reduces image noise but also improves spatial resolution.
55
Figure 27: Image obtained with FBP reconstruction (Left) and AIDR 3D reconstruction (Right) Philips: iDose4
iDose4 is Philips iterative reconstruction technique. According
to Philips, iDose4 improves spatial resolution at low dose,
reduces noise, prevents artifacts, and improves image quality
at low dose.54 Furthermore, Philips asserts that iDose4
reconstruction can be achieved in seconds rather than
minutes.55
56
Figure 28: Image obtained with FBP reconstruction (Left) and iDose4 reconstruction (Right)56
Business Model
Trial Period
A short trial period business model is being designed to help Plexar get
exposure in the CT industry and most probably generate some revenue. The
customer in this business model is CT scan centers, and the model follows the
Freemium principle, which will lead to premium model.
Freemium
Freemium is a business model by which a proprietary product or
service is initially provided free of charge, but money is charged for
advanced features/ functionality or extensive use. In its follow
Freemium model, Plexar has to develop software that outputs a dose
variability graph. The software should be user-friendly so that a 57 radiologist does not have to spend time to learn it. Such software can be given to CT scan centers for free. However, the software must expire after certain time point or after certain number of uses.
Figure 29: Freemium and Premium Business Model Most of the CT scan centers do not know that they have dose variability problem. Furthermore, if they have a dose variability problem, they do not know how big the problem is. The free software would help them realize the depth of the problem without spending a huge amount of money and time.
58
Figure 30: Workings of Free Dose Assessment Software The free software works as shown in the Fig. 30. PI’s customer can choose one machine, one protocol, and one operating mode. By doing so, they will be able to import their last 50-100 scans. The dose assessment software will output Dose vs. Patient size graph.
Figure 31: Freemium to Premium If their graph looks like the one shown in Fig. 31 then they have a problem. They can do two things:
1. They may want to test other machines or other protocols. If
they want to do so they have to buy premium version of the
Dose Assessment software.
2. They may want to have a solution for the problem. If they want
solution then they have to consider ConvergeCT.
59 ConvergeCT
ConvergeCT is PI’s solution for dose variability. The detailed workings of
ConvergeCT have been discussed in Plexar’s Technology section.
The effective revenue model for this product is pay per scan. This model
provides a recurring source of income. Plexar should follow customer
solution profit model:
Customer Solution Profit Model (CSP)
The mantra of CSP is to invest time, money, and energy in learning a
customer’s need and the customizing solutions according to that need.
For constructing “green line”, Plexar needs to work with the
radiologists to understand their requirement. Thus, CSP perfectly
captures the workflow of Plexar.
Figure 32: Customer Solution Profit In Fig. 32 the y-axis represents money and the x-axis represents time.
If PI follows CSP then it needs some initial funding to get through that
cash valley. The cash valley is due to the initial cash, time, and
manpower investment needed to understand customers’ need. 60 Pay per Scan Model
On the basis of the data available for number of total CT procedures
performed in the USA, Japan, and the UK, the market size is calculated
to be about 151 million. If PI charges five dollars per scan (minimum)
and captures 5% market then the total revenue will be about $38
million.
Dose Reduction Quantifier (DRQ)
Dose Reduction Quantifier is a tool that can be used by CT OEMs as well as
regulatory bodies like FDA. Here, PI will work as a consultant.
CT OEMs
CT OEMs can use this tool to verify their dose reduction claims. PI has
charge them per machine basis or per project basis. The fee structure
will be based on the agreement between PI and OEM.
FDA
PI can help FDA to certify CT scan machine by verifying the dose
reduction claims. PI can charge FDA on per consultation basis.
61 SWOT Analysis
SWOT analysis is a useful technique for building strategies by comprehending company’s strengths and weaknesses, and by identifying opportunities open to the company and threats over the company.
Strengths
PI has an experienced team:
David Rohler has over 30 years of experience with imaging
development for medical devices and instrumentation products. He is
co-inventor on 12 patents and co-author on six papers.
Thomas Toth worked for GE Healthcare as a principal engineer for 22
years, focusing on CT image quality and dose reduction. He has 92 US
patents, 19 scientific journal articles and 11 conference presentations..
He is active in a variety of national and international medical imaging
groups including: International Society for Optical Engineering,
Physics of Medical Imaging, International Electro Technical
Commission - CT Maintenance Group, Alliance for Radiation Safety in
Pediatric Imaging.
Steven Izen is a Professor of Mathematics at Case Western Reserve
University (CWRU). He has been working on CT reconstruction
algorithms and imaging problems since 1986. Moreover, he has
62 worked with researchers at University Hospitals of Cleveland, the
Metrohealth Medical Center of Cleveland, and the NASA-Glenn
Research Center. He has published over 20 journal articles on
imaging-related topics.
Bruce E. Terry is a lecturer in the Science and Technology
Entrepreneurship program at CWRU. He was President and CEO of
Mayfran International Inc. for almost 17 years. During his 25-year
tenure at Mayfran, he won dozens of multi-million dollar contracts;
drove the company’s market penetration in Japan and Europe; and
negotiated numerous successful global license and technology
agreements, joint ventures, partnerships, and acquisitions. He is an
active member of the North Coast Angel Fund.
Arjun Maniyedath is a research scientist at PI and PhD student at
Cleveland State University and Cleveland Clinic. He has a strong
background in software and hardware engineering and hands-on
experience with many facets of the x-ray equipment business
including installation, testing, maintenance, user training, and service
training.
PI’s technology is platform independent. It works on older machine as well as newer machines made by any manufacturer. PI has developed its technology
63 in such a way that it helps to reduce dose variability as well as it can be used
as quality assurance system.
Weaknesses
PI’s products are still in the research and development phase. Thus, PI does
not have a finished product yet. Moreover PI is running out of money. PI not
only needs money to do more R&D it needs money to do product
development (i.e. software development). The lack of money is not letting PI
to hire more R&D manpower, which is slowing its research and product
development. PI does not have a single full-time employee yet. The lack of a
full-time team further slows down the R&D process.
PI has not focused on getting more exposure in the CT industry, which is
evident from lack of a good informative website about PI’s work. PI does not
have a single issued patent yet, making it more difficult to get funding.
Even though PI has talked with Toshiba, PI has not been able to establish a
Dose Reduction Quantification service contract with any OEM. A deal with an
OEM would be a great way to get early revenue and exposure. Therefore, PI
must focus on getting a deal with OEM.
Opportunities
Dose variation is a problem that was not widely known until the Pulitzer
Prize winning investigative journalist, Walt Bogdanich, wrote a lengthy series
in the New York Times. Today, people are pretty well informed about dose
64 variation. There is no known product on market addressing the problem that
can work on all CT machines; accordingly a large opportunity exists.
PI can also still land some non-dilutive funds. National Institutes of Health
(NIH) has a grant titled “Decreasing Patient Radiation Dose from CT Imaging:
Achieving Sub-mSv Studies (U01)”, which PI can apply for; and in Cleveland
PI still has the chance to receive a $200K convertible note from JumpStart.
Threats
Two years ago, CT OEMs with iterative reconstruction features were
mistakenly thought to be the major threats to PI, but now the products like
the Guidance System and Duke’s QA software (DQA) looks like more realistic
threats to PI. However, there is not any public information about the
commercialization of either the Guidance System or DQA.
PI has two distinct customer segments: OEMs and CT scan centers. CT OEMs
are always trying to design software and hardware so as to reduce dose
variability. However, CT OEMs tend to resist third party products and will try
to prove that their own products are the best. Since OEMs are financially
strong and independent, PI will have hard time cracking this segment with
ConveregeCT, but has a viable chance with DRQ.
65 If PI approaches CT scan centers and tells them they have a problem with
dose variability, the CT scan centers will likely be resistant and secretive. CT
scan centers may try to ask the OEMs about ConvergeCT. It is very unlikely
that the OEMs will advise CT scan centers to use PI’s products. Thus, PI must
get traction by demonstrating compelling evidence of the need for
ConvergeCT. That is what the Freemium model is designed to do.
SO Strategies
SO strategies use strengths to help exploit opportunities. Previously, PI has
secured the Innovation Fund, North Coast Opportunities Technology Fund,
and GLIDE Innovation Fund. PI’s past record indicates that PI can convince
investors. PI can repeat history by capturing an NIH grant and seek funding
from JumpStart and private investors.
PI’s team understood the problem of dose variability and thought of a unique
way to solve it. Since conception, PI’s product has evolved greatly. As a result
of significant amount of R&D, PI can claim that they have most logical and
complete solution for dose variability.
WO Strategies
WO strategies use opportunities to deal with weaknesses. In the case of PI,
most of the weaknesses are related to funding. Money can help PI to get more
R&D manpower and fulltime employees. Additional talented and dedicated
66 manpower will certainly accelerate the product development process. PI can
get money from NIH grant. PI should focus on getting NIH grant.
ST Strategies
ST strategies use strengths to eliminate threats. OEMs are only focused on
reducing dose variability in newer machines. Since PI’s technology is
machine and manufacturer independent, it can solve problems in any
machines. Furthermore, PI’s DRQ can be used as tool to verify the dose
reduction claim that OEMs make and PI may be able to get inroads with the
FDA. PI would then have some leverage over OEMs.
WT Strategies
WT Strategies are about establishing a defensive plan to prevent the
company’s weaknesses from making it highly susceptible to outside threats.
PI has filed for international patent protection, which has to be the major
factor of its defensive plan.
67 Table 9: SWOT Analysis
Strength-S Weakness-W
S1: Experienced Team W1: Funding S2: Platform independent W2: No product yet technology W3: Not enough S3: Works on any CT manpower for R&D machines W4: Lack of exposure in S4: Can reduce dose CT Market variation significantly W5: No deals with OEMs W6: No utility Patents issued W7: No fulltime employees
Opportunities-O SO Strategies WO Strategies
O1: Dose variation is a S1-O1: Experienced team W1,3-O2: Funding can problem that needs to be can solve dose variation solve money problem solved, so there is an problem and capture the and can get some R&D opportunity market manpower O2: Non dilutive funding* S-ALL-02: Become more aggressive seeking non- dilutive funding Threats-T ST Strategies WT Strategies
T1: First to Market S2-T1: Platform PI’s utility patent product (Like: Guidance independent tech can application if approved System) beat the competition may protect it T2: CT OEMs resist S3-T2: Manufacturers are outside technology introducing dose T3: Customer resistance reduction only in the newer machine, but Plexar’s tech can be implemented in old as well as new machines.
* http://grants.nih.gov/grants/guide/pa-files/PAR-12-206.html
68 Five Forces Analysis
Michael Porter introduced the concept of the “Five Forces Competitive Position”, which helps to analyze the competitive position of a company.
Threats of New Entrants
The threat of new entrants is almost inevitable in any market. High barrier to
entry can help to avoid or delay threats. There are already two (Guidance
System and Duke’s QA software) similar technologies being researched and
developed by research and academic groups. These two technologies pose a
threat to PI. Strong mathematics, imaging, and software development
background are needed to develop products in this space. Academic groups
with aforementioned skills can create products that can be threats to PI. The
only way avoid the threat of new entrants is to create a steep learning curve,
which can be achieved by R&D and patent protection. Therefore, PI should
focus on R&D and commercialize its product as soon as possible.
Threat of Substitutes
Threat of substitutes is low when switching costs are high. Switching cost is
defined as a one-time cost a customer incurs when switching to new product
or service. Pricing a product at a low price, delivering high quality products,
and strong customer service can result in high switching costs. PI should
focus on developing a robust and inexpensive product. Furthermore,
69 intellectual property (IP) is very important. IP can take care of products that
try to imitate PI’s product. PI must focus on getting a strong utility patent.
Bargaining Power of Buyers
The bargaining power of buyers can be defined as the ability of customers to
put the firm under pressure. Thus, it is always beneficial for a firm to lower
the bargaining power of buyers. Bargaining power of buyers is low when:
1. There are many buyers. Dose variability is still an unsolved
problem. CT scanner OEMs are trying to attack the problems by
introducing new CT scanners with Iterative Reconstruction,
seemingly avoiding old scanners. PI has a solution that can work
on all scanners and the beauty is that ConvergeCT is still needed in
iterative schemes. Therefore, PI can address large the entire CT
scan market.
2. Buyers have few options. PI has two customer segments: OEMs
and CT scan centers. OEMs are trying to develop their solutions
and they will try to resist PI’s product. Since PI’s product works on
all machines, it can be used as a QA product. For PI’s product to be
a QA product PI must eventually convince the FDA. Thus, PI’s
penetration into OEMs market is contingent on convincing FDA. In
last couple of years two other research groups have developed
solutions that is similar to PI’s solution. However, those groups
haven’t commercialized their products yet. PI can still have a 70 competitive edge if it can launch product before these two groups.
Ultimately in future, there will be products from these two groups,
which will give more options to Buyers.
Bargaining Power of Suppliers
The bargaining power of suppliers acts exactly as the bargaining power of
buyers but in the opposite direction. Since PI’s business is more of a service
than selling physics products, the bargaining power of suppliers is irrelevant.
However, PI needs R&D staff, which it has not done good job of acquiring. PI
should try to add some talents (in the area of software development). The
suppliers of those talents are research and engineering schools (like: Case
Western Reserve University (CWRU)). Therefore, PI should establish a good
relationship with CWRU’s engineering departments to acquire the best talent.
Rivalry Among Existing Competitors
The fundamental profitability equation: Profit = Price – Cost is highly affected
by rivalry force because it affects both price and cost in negative ways. As
discussed earlier that there are some competitors that are conducting similar
research as PI. Moreover, OEMs are always trying to come up with their own
solutions. Therefore, PI will face fierce competition. However, PI has
managed to place itself in a unique position. It can make any scanner better.
In that sense, PI has a competitive edge.
71 Appendix Signal Detection Theory
A primary goal of medical imaging is to assist physicians in the diagnostic process. An image quality (IQ) should be a measure of how well the medical image fulfills that purpose. Thus, IQ is task specific. So, an obvious question to ask is:57
How well, given the images, can the specific diagnostic task be performed by a
physician?
To answer this question, a brief introduction to signal detection theory is needed:
Image58
Mathematically, image can be modeled by:
� = ℋ� + �
where ℋ represents the imaging system, n represents the detector noise, and �
represents the object being imaged.
In a statistical approach to signal-detection tasks, two hypotheses exist: the signal
present hypothesis H1, and the signal-absent hypothesis H0. Under the H1 and H0
hypotheses, the image data are given respectively by:
�!: � = ℋ� + ℋ�� + �
�!: � = ℋ� + �
where �� represents potentially random signal to be detected.
72 Observer59
Human observer (reader) studies are the most obvious and most widely used task-
based assessment of IQ. In reader studies, the observers (often true medical
experts) read a set of test images and make a diagnostic decision for those images.
However, reader studies are time consuming and expensive.
As an alternative, mathematical model observers may be used. An ideal observer
(IO), a type of mathematical observer, is one whose signal detection performance
can be used to rank imaging systems or normalize the performance of other
observers, such as humans. However, in practice it is difficult to estimate or
derive an ideal observer because of the high dimension and great complexity of
the image statistics that are unknown and poorly estimated for real clinical data
sets.
In signal detection theory, a model observer makes decisions by evaluating a
discriminant function t, which maps an image g to a number τ, called the test
statistic:
� = �(�)
A signal present or signal absent decision is made by comparing the test statistic
with a threshold.
An ideal observer (IO) is the one whose discriminant function maximizes
sensitivity (detection rate) at every level of specificity (false alarm rate). The
discriminant function of IO is: 73 � �|� � � = ! � �|�!
This function is known as likelihood ratio. The problem with this observer is that
in practice the probabilities that make up the likelihood ratio are often
complicated or unknown. Thus, an alternative IO, which is optimal in some way
and practical, is needed. Such an alternative is called Ideal Linear Observer
(ILO)*. Its discriminant function involves only the means and the inverse
covariance matrices of the data under the two hypotheses.
In practice, the ILO must be estimated or trained from sample images. If M X M
image is considered then the covariance matrix is N X N, where N = M2 is the
number of pixels. For accurate estimate of the covariance matrix, one rule of
thumb is to choose Ntrain to be 10-100 times N. Thus, even for small 64 X 64
images, more than 40,000 images are needed to train the ILO. Therefore, ILO
becomes impractical for medical images.
Again, an alternative method is needed to simplify ILO. By constraining the ILO
to a small set of linear channels, ILO can be simplified. This observer is called the
Channelized Hotelling Observer (CHO). The choices for channels were motivated
by psychophysical evidence that humans process image information through
linear spatial-frequency selective channels.
* Ideal Linear Observer is also known as Hotelling Observer (HO) 74
Figure 33: Diagrammatic Representation of CHO CHO is the ILO constrained to the output of J linear channels, where J << N. The main appeal of CHO is that it dramatically reduces the dimension of the problem to a size J that is much smaller than the original image. Instead of images g, an observer’s test statistic τ is a linear combination of the elements of a J- dimensional random vector v = Utg, where U is an N X J matrix. In this formulation, a channel is column of the matrix U, making each channel an N- dimensional image, denoted as uj:
U = [u1, u2,…, uj]
The output of the jth channel is nothing more than the scalar product of uj and g, given by
75 ! ! �! = [��]!�! = (�!) � !!!
Here it is evident that a channel acts like a linear observer. The weighting of the channel outputs that have maximum SNR is expressed as a template in channel space, namely,
!! �� = �! ��
In this expression sv is the J-dimensional mean difference of channel outputs, and
Cv is the J X J average channel covariance matrix.
�� = �� − ��
�! = � �! for m = 0,1
! 1 � = � − � � − � ! � ! 2 � � ! !!!
By the linearity of the expectation operator, sv and Cv are related to the image statistics s and Cg as follows:
! �� = � �
! �! = � �!�
Thus, the template of the CHO can be considered as a J-dimensional vector in the channel space acting on v or N-dimensional vector in the original image acting on g:
76 t −1 tv (v) = (sv ) Cv v
t t −1 t t(g) = s U (U CgU) U g
Why use channels?60
The primary challenge is that images are made up of many elements; a small
image might have M X M = 64 X 64 = 4096 elements. This means that Cg is 4096
X 4096, making it is too large for most numerical inversion methods. A minimum
of 4096 images are needed to have any chance of inverting a 4096 X 4096
estimated covariance matrix. Even more images should be used to get an accurate
-1 estimate of Cg .
The main appeal of the CHO is that it dramatically reduces the dimension of the
problem to size J that is much smaller than the original image. The smaller
covariance matrix of the channel outputs Cv is easily inverted by matrix inversion
software. Furthermore, it takes fewer sample images to estimate Cv accurately.
How are channels chosen?61
Since dimensionality reduction runs the risk of information loss, the most
important step in developing CHO is the choice of the channels. A secondary
consideration is that the channels be robust and practical. In other words, the same
channels should be applicable in many situations.
77 Given a set of channels and an adequate training set of images, the signal to noise
(SNR) ratio can be calculated (SNRv). The goal is to estimate SNRI (Signal to
Noise Ratio for ILO). Thus, channels are chosen such that SNRv≈ SNRI.
78 Bibliography
1 WolframAlpha, Accessed November 25, 2011. http://www.wolframalpha.com/input/?i=what is need? 2 Smith-Bindman, Rebecca. University of California San Fransico. web, http://www.youtube.com/watch?feature=player_embedded&v=7Fx44yPkkGU. 3 Ibid. 4 Ibid. 5 AAPM, "AAPM Computed Tomography Radiation Dose Education Slides." Accessed June 10, 2013. www.aapm.org/pubs/CTProtocols/documents/EducationSlides.pptx. 6 IMV, "IMV 2012 CT Market Outlook Report." Accessed June 10, 2013. 7 Hanley, Mike. "X-Ray Risk." Accessed June 10, 2013. http://www.xrayrisk.com/faq.php. 8 National Cancer Institute, "Radiation Risks and Pediatric Computed Tomography (CT): A Guide for Health Care Providers." Accessed June 10, 2013. http://www.cancer.gov/cancertopics/causes/radiation/radiation-risks-pediatric-CT. 9 (Hanley Mike 2012) 10 Ibid. 11 Ibid. 12 Gorski, David. "Radiation from medical imaging and cancer risk." Accessed June 10, 2013. http://www.sciencebasedmedicine.org/index.php/radiation-from-medical-imaging- and-cancer-risk/. 13 Ibid. 14 WorldCulturePictorial, Accessed June 10, 2013. http://www.worldculturepictorial.com/blog/content/ct-scan-study-shows-increased- radiation-exposure-cancer-risks-tests-often-unnecessary. 15 Smith, Steven. The Scientist and Engineer's Guide to Digital Signal Processing. http://www.dspguide.com/ch25/5.htm (accessed June 10, 2013). 16 Hendee, William, and E Ritenour. Medical Imaging Physics, http://ymk.k- space.org/CT_chapter15.pdf (accessed June 10, 2013). 17 Ibid 18 Ibid 19 Timothy Feeman, The Mathematics of Medical Imaging: A Beginner’s Guide, pp.3 (accessed June 10, 2013). 20 Klanner, Robert. Accessed June 10, 2013. http://www.uke.de/kliniken/strahlentherapie/downloads/klinik-strahlentherapie- radioonkologie/Diplomarbeit_Urban.pdf. 21 Goldman, Lee. "Principles of CT: Radiation Dose and Image Quality." Accessed June 10, 2013. http://tech.snmjournals.org/content/35/4/213.full. 22 Ibid. 23 McNitt-Gray, Michael. "Tradeoffs in CT Image Quality and Dose." Accessed June 10, 2013. http://www.oucom.ohiou.edu/ou- microct/Downloads/Tradeoffs_in_CT_Image_Quality_and_Dose_9794-13379.pdf.
79
24Goldman, Lee. "Principles of CT: Radiation Dose and Image Quality." Accessed June 10, 2013. http://tech.snmjournals.org/content/35/4/213.full. 25 Ibid. 26 Ibid. 27 Goldman, Lee. "Principles of CT: Radiation Dose and Image Quality." Accessed June 10, 2013. http://tech.snmjournals.org/content/35/4/213.full. 28 Ibid. 29 (McNitt-Gray Michael 2013) 30 Ibid. 31 Ibid. 32 BCC Research, "Global markets for CT scanners." Accessed June 10, 2013. 33 Ibid. 34 Ibid. 35 Ibid. 36 Ibid. 37 Ibid. 38 Ibid. 39 (IMV 2012) 40 Larson, David. "Method for consistent and verifiable optimization of computed tomography (ct) radiation dose." Accessed June 21, 2013. https://www.google.com/patents/WO2013049818A1?cl=en&dq=cincinnati children's hospital 2009 david larson&hl=en&sa=X&ei=dpOiUa6wJ7i34AOijoHYDw&ved=0CDQQ6AEwAA. 41 McCollough, Cynthia, Andrew Primak, and Natalie Braun. "Strategies for Reducing Radiation Dose in CT." Accessed June 10, 2013. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2743386/. 42 Ibid. 43 Ibid. 44 Ibid. 45 Ibid. 46 Ibid. 47 Siemens, "Iterative Reconstruction in Image Space (IRIS) in Clinical Practice." Accessed June 12, 2013. http://www.medical.siemens.com/siemens/en_US/gg_ct_FBAs/files/Case_Studies/CT_IR IS_final.pdf. 48 Ibid. 49 Kaplan, Abrams. "Iterative Reconstruction in CT Evolves for Lower Dose, Increased Clarity.” Accessed June 12, 2013. http://www.diagnosticimaging.com/iterative- reconstruction-ct-evolves-lower-dose-increased-clarity/page/0/2 50 (Siemens 2013) 51 (Kaplan Abrams 2013) 52 (Siemens 2013)
80
53 Toshiba, “AIDR 3D Reduces Dose and Simultaneously Improves Image Quality.” Accessed June 12, 2013. http://www.toshiba-medical.eu/upload/TMSE_CT/White Papers/White Papers/Toshiba_White paper CT_nov11.pdf?epslanguage=en. 54 Philips, Accessed June 12, 2013. http://www.healthcare.philips.com/main/products/ct/products/dosewise/. 55 Ibid. 56 (Kaplan Abrams 2013) 57 Platisa, Lijiljana, Bart Goossens, and Ewout Vansteenkiste.Channelized Hotelling observers for the assessment of volumetric imaging data sets. working paper., 2011. 58 Kupinski, Matthew, and Eric Clarkson. Extending the channelized Hotelling observer to account for signal uncertainty and estimation tasks. working paper., The University of Arizon Tuscon, 2005. 59 Gallas, Brandon, and Harrison Barrett. Validating the use of channels to estimate the ideal linear observer. working paper. 60 Ibid. 61 Ibid.
81