Turner Dissertation_Final

UNIVERSITY OF CALIFORNIA
Los Angeles
The Development of Methods to Assess Radiation Dose to Organs
from Multidetector Computed Tomography Exams
Based on Detailed Monte Carlo Dosimetry Simulations
A dissertation submitted in partial satisfaction of the
requirements for the degree Doctor of Philosophy
in Biomedical Physics
by
Adam Christopher Turner
2011

© Copyright by
2011

ii
The dissertation of Adam Christopher Turner is approved.
Christopher Cagnon
John DeMarco
Matthew Brown
David Saltzberg
Michael McNitt-Gray, Committee Chair
University of California, Los Angeles
2011

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I dedicate this dissertation to my parents Gary and Marilynn Turner.
I owe you everything.

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Table of Contents
Chapter 1 Background and Motivation.......................................................................................1
1.1 Radiation Risks from CT Exams ...........................................................................................4
1.2 Routine Clinical CT Dosimetry Assessment (CTDI and DLP) .............................................6
1.3. Limitations of the CTDI......................................................................................................10
1.4 Effective Dose from CT Exams and its Limitations............................................................11
1.5. Existing Organ Dose Estimation Methods..........................................................................13
1.6. Discussion...........................................................................................................................18
Chapter 2 Specific Aims..............................................................................................................19
Chapter 3 UCLA Monte Carlo MDCT Dosimetry Package....................................................21
3.1 Radiation Transport Methods ..............................................................................................21
3.2 Modifications to Model MDCT Scanners............................................................................22
3.3 Post Simulation Processing..................................................................................................24
3.4 Validation of Dose Simulations...........................................................................................25
Chapter 4 A Method to Generate Equivalent MDCT Source Models Based on
Measurements ..............................................................................................................................27
4.1 Introduction..........................................................................................................................27
4.2 Methods ...............................................................................................................................29
4.3 Results..................................................................................................................................44
4.4 Discussion............................................................................................................................49
Chapter 5 The Feasibility of Scanner-Independent CTDIvol-to-Organ Dose Coefficients ....57
5.1 Introduction..........................................................................................................................57
5.2 Methods ...............................................................................................................................58
5.3 Results..................................................................................................................................66
5.4 Discussion............................................................................................................................73
Chapter 6 Size Dependence of CTDIvol-to-Organ Dose Coefficients.......................................80
6.1 Introduction..........................................................................................................................80
6.2 Methods ...............................................................................................................................81
6.3 Results..................................................................................................................................91
6.4 Discussion............................................................................................................................97

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Chapter 7 Estimating Dose to Partially-Irradiated Organs using CTDIvol -to-Organ Dose
Coefficients .................................................................................................................................105
7.1 Introduction........................................................................................................................105
7.2 Methods .............................................................................................................................108
7.3 Results................................................................................................................................114
7.4 Discussion..........................................................................................................................121
Chapter 8 The Feasibility of CTDIvol -to-Organ Dose Coefficients that Account for Tube
Current Modulation...................................................................................................................126
8.1 Introduction........................................................................................................................126
8.2 Methods .............................................................................................................................130
8.3 Results................................................................................................................................139
8.4 Discussion..........................................................................................................................148
Chapter 9 Advanced MDCT Monte Carlo Dosimetry Validation Methods.........................154
9.1 Introduction........................................................................................................................154
9.2 AAPM Task Group 195.....................................................................................................157
9.3 Half Value Layer and Bowtie Profile Measurements as Benchmarks...............................181
9.4 Surface Dose Measurements on a Thorax Anthropomorphic Phantom.............................191
9.5 Conclusions........................................................................................................................203
Chapter 10 Dissertation Summary and Conclusions..............................................................208
Appendix A. Supplementary Tables from Chapter 4.............................................................212
Appendix B. Energy Dependence of Small Volume Ionization Chambers and Solid State
Detectors at Diagnostic Energy Ranges for CT Dosimetry – Assessment In Air and In
Phantom......................................................................................................................................219
Appendix C. Summary of Organ Dose Estimation Method..................................................224
References...................................................................................................................................229

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List of Figures
Figure 1.1 CT images of various anatomical regions including A) abdomen, B) chest, C) head....1
Figure 1.2 A) Diagram of a third-generation CT scanner including the rotating x-ray source,
rotating detector array, and the translating table. B) Illustration of the x-ray source path for a
helical CT scan1...............................................................................................................................2
Figure 1.3 Probability distribution function (PDF) of a typical tungsten anode x-ray tube spectrum
for a tube voltage of 140 kV (i.e. 140 kVp) used for CT scanners. .................................................3
Figure 1.4 The longitudinal dose profile from a contiguous axial exam. The profile for each
rotation and the summation is shown. Reprinted from C.H. McCollough, et al.11
..........................7
Figure 1.5 16 cm diameter ―head‖ and 32 cm diameter ―body‖ CTDI phantoms composed of
PMMA and containing pre-drilled holes at center and four periphery positions.............................9
Figure 1.6 A) Screen shot from the ImPACT Dosimetry Calculator showing the MIRD
mathematical phantom used by the NRPB Monte Carlo Study. B) Adult females from GSF
Family of Voxelized Models. ........................................................................................................16
Figure 4.1 Diagram of HVL measurement set up that utilizes a stationary (non-rotating) x-ray
source.............................................................................................................................................32
Figure 4.2 Diagram of bowtie profile measurements that characterize the attenuation across the
fan beam.........................................................................................................................................33
Figure 4.3 Illustration of method for generating equivalent spectrum from measured..................36
Figure 4.4 The cumulative percentage of CTDI100 simulations that are characterized by the level
agreement with measured CTDI100 values specified by each category: (1: ≤±1% 2: >±1% but
≤±2% 3: >±2% but ≤±5% 4: >±5% but ≤±10% 5: >±10)...........................................................49
Figure 5.1 of Irene from the GSF Family of Voxelized Models. Note the individual segmentation
of radiosensitive organs. ................................................................................................................61
Figure 5.2 Organ dose (DS,O), in mGy, and effective dose (DS,ED), in mSv, for a 100 mAs/rot scan
for scanners 1–4.............................................................................................................................68
Figure 5.3 CTDIvol, S normalized organ (nDS,O), and effective (nDS,ED) doses for scanners 1–4....71
Figure 6.1 Fig. 1. Illustrations of the GSF Family of Voxelized Phantoms as described in
Petoussi-Henss, Zankl, et al.39
and Fill, Zankl, et al.40
. Additional information provided in Table
6.1. .................................................................................................................................................83
Figure 6.2 Mean CTDIvol normalized organ doses across scanners as a function of patient
perimeter (in cm). The exponential regression curve, equation, and correlation coefficient for
stomach is shown as an example. ..................................................................................................93

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Figure 6.3 The proposed method to estimate patient-, scanner-, and exam-specific organ dose
using the size coefficients (AO, BO), patient perimeter (in cm), and the CTDIvol reported by the
scanner. ........................................................................................................................................104
Figure 7.1 Illustration of the process to segment partially-irradiated organs into "in-beam" and
"out-of-beam" segments...............................................................................................................110
Figure 7.2 CTDIvol normalized dose values for the in-beam segment of each partially-irradiated
organ as a function of patient perimeter in cm. The exponential trendline for bone surface is
shown as an example. ..................................................................................................................117
Figure 7.3 Diagram of the proposed method to estimate patient-, scanner-, and exam-specific
dose to partially-irradiated organs using the size coefficients (AO,in, BO,in), average percent
coverage (αorgan), patient perimeter (in cm), and the CTDIvol.......................................................124
Figure 8.1 Tube current function illustrating modulation of the tube current (mA) in the axial
plane (high-frequency oscillations) and along the longitudinal plane (low-frequency oscillations).
.....................................................................................................................................................126
Figure 8.2 An anonymized dose report for an exam performed with TCM on a Siemens Sensation
64 located at UCLA. For this exam, the first scan was a used to generate a two-dimensional
planning image called a ―topogram‖. Then, two helical scans were performed and information
including the kVp, average mAs, TCM reference mAs, and CTDIvol for both is included in the
report............................................................................................................................................128
Figure 8.3 Generation of a voxelized model: (a) original patient image, (b) radiologist‘s contour
of the breast region, (c) threshold image to identify glandular breast tissue and (d) the resulting
voxelized model. Reprinted from Angel, et al.61,62
. .....................................................................133
Figure 8.4 (mean organ dose/CTDIvol across scanners) from fixed tube current scans as a
function of patient perimeter (in cm) for lung and glandular breast tissue. The exponential
regression curves for each organ are also shown.........................................................................140
Figure 8.5 (mean organ dose/CTDIvol across scanners) from fixed tube current scans as a
function of patient perimeter (in cm) for liver, spleen, and kidney. The exponential regression
curves for each organ are also shown. .........................................................................................140
Figure 8.6 Simulated organ dose values in mGy from simulations of Siemens Sensation 64 chest
exams performed with TCM as a function of perimeter in cm for lung and glandular breast tissue.
.....................................................................................................................................................142
Figure 8.7 Simulated organ dose values in mGy from simulations of Siemens Sensation 64
abdomen/pelvis exams performed with TCM as a function of perimeter in cm for liver, spleen,
and kidney....................................................................................................................................142

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Figure 8.8 A) Lung dose estimates calculated with CTDIvol,Avg mAs and lung doses from TCM
simulations. B) Percent error of lung dose estimates calculated with CTDIvol,Avg mAs with respect to
lung doses from TCM simulations...............................................................................................144
Figure 8.9 kP,O (simulated organ dose/estimated organ dose) as a function of patient perimeter (in
cm) for lung and glandular breast tissue. The linear regression curves for each organ are also
shown...........................................................................................................................................146
Figure 8.10 kP,O (simulated organ dose/estimated organ dose) as a function of patient perimeter
(in cm) for liver, spleen, and kidney. The linear regression curves for each organ are also shown.
.....................................................................................................................................................146
Figure 8.11 The proposed method to estimate patient-, scanner-, and exam-specific organ dose
using the size coefficients (AO, BO), TCM correction factor coefficients (CO, DO) patient
perimeter (in cm), and the CTDIvol corresponding to the Quality Reference mAs......................151
Figure 9.1 Diagram of the simulation geometry used to simulate HVL and QVL measurements as
defined by Task Group 195..........................................................................................................164
Figure 9.2 Diagram of CTDI-like phantom simulation as defined by AAPM Task Group 195..170
Figure 9.3 Diagram of CTDI-like phantom. Note the two CTDI rod-like inserts and the first
projection angle............................................................................................................................171
Figure 9.4 Diagram of the contiguous axial tally regions for the Test 1. For these simulations the
source is fixed and located at the longitudinal center of the phantom (z=0). ..............................172
Figure 9.5 MCNPX simulated kerma for the axial segments of CTDI-like phantom from the
monoenergetic beam in keV/kg/NPS for the narrow and wide beam widths. .............................175
Figure 9.6 MCNPX simulated kerma for the axial segments of CTDI-like phantom from the
polyenergetic beam in keV/kg/NPS for the narrow and wide beam widths. ...............................176
Figure 9.7 MCNPX simulated kerma tallied in the center and peripheral rods from fixed source
positions at gantry angles ranging from 0 to 360 degrees on a logarithmic scale........................177
Figure 9.8 Diagram of the set up used to measure the HVL and QVL for both Scanners 1 and 2.
The x-ray source remained stationary at the 6o'clock position....................................................185
Figure 9.9 Diagram of the set up used to measure the bowtie profile for both Scanners 1 and 2.
The x-ray source remained stationary at the 3 o'clock position...................................................186
Figure 9.10 Percent error of bowtie profile simulations as a function the distance from isocenter
(in cm) for Scanner 1. ..................................................................................................................188
Figure 9.11 Percent error of bowtie profile simulations as a function the distance from isocenter
(in cm) for Scanner 2. ..................................................................................................................189
Figure 9.12 The Alderson Chest/Lung Phantom from Radiological Support Devices, INC.78
...192

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Figure 9.13 Generation of a voxelized model: (a) original patient image, (b) radiologist‘s contour
of the breast region, (c) threshold image to identify glandular breast tissue and (d) the resulting
voxelized model. Reprinted from Angel, et al.61,62
. .....................................................................194
Figure 9.14 Axial view of the voxelized model created from images of the Alderson Lung/Chest
Phantom. ......................................................................................................................................195
Figure 9.15 Sagital view of the voxelized model created from images of the Alderson Lung/Chest
Phantom. ......................................................................................................................................195
Figure 9.16 Coronal view of the voxelized model created from images of the Alderson
Lung/Chest Phantom....................................................................................................................196
Figure 9.17 The measured and simulated doses to the ionization chamber located on the surface
of the thorax phantom as a function of tube start angle...............................................................199
Figure 9.18 Diagram to illustrate how a lateral shift results in a phase shift and amplitude change
for dose as a function of tube start angle plot. .............................................................................202
Figure 9.19 Proposed approach for robustly validating the accuracy of a Monte Carlo CT
simulation package. Starting at the top, each level introduces a new level of complexity in order
to assess a different component of the simulation package. ........................................................207

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List of Tables
Table 1.1 ICRP Publication 103 recommended tissue weighting factors.5
....................................12
Table 1.2 Normalized effective dose per dose-length product (DLP) for adults (standard
physique) and pediatric patients of various ages over various body regions. Conversion factor for
adult head and neck and pediatric patients assume use of the head CT dose phantom (16 cm). All
other conversion factors assume use of the 32-cm diameter CT body phantom3
..........................13
Table 6.1 Information about the GSF Family of Voxelized Models as described in Petoussi-
Henss, Zankl, et al.39
and Fill, Zankl, et al.40
.................................................................................83
Table 6.2 Mean CTDIvol normalized organ doses across scanners for each patient model for fully-
irradiated organs. Note that the gall bladder was not included in the Child patient model. ..........92
Table 6.3 Results of exponential regression analysis describing as a function of perimeter
(cm) for fully-irradiated organs. ....................................................................................................94
Table 6.4 Mean CTDIvol normalized organ doses across scanners ( ) for each patient model
for partially-irradiated organs. A dash indicates the organ was not included in the patient model.
.......................................................................................................................................................94
Table 6.5 Percent coverage of each partially-irradiated organ (i.e. percentage of organ volume
located within the abdominal scan region). The last two columns report the average and standard
deviation across patient models. A dash indicates that the organ was not included for the given
patient model..................................................................................................................................95
Table 6.6 Average and standard deviation of the percent coverage of each partially-irradiated
organ and the correlation coefficient resulting from the exponential regression relating to
perimeter........................................................................................................................................96
Table 6.7 Percent ratios of dose to each non-irradiated organ relative to average fully-irradiated
organ dose. The last two columns report the average and standard deviation across patient
models. A dash indicates that the non-irradiated organ was not included for the given patient
model. ............................................................................................................................................97
Table 7.1 CTDIvol normalized dose to the in-of-beam portion of each partially-irradiation organ.
(i.e. ). Note that the esophagus was not included in the Child model and the small
intestine was fully-irradiated in the Baby model. ........................................................................115
Table 7.2 CTDIvol normalized dose to the out-of-beam portion of each partially-irradiation organ.
(i.e. ). Note that the esophagus was not included in the Child model and the small
intestine was fully-irradiated in the Baby model. ........................................................................115
Table 7.3 Ratio (expressed as a percent) of CTDIvol normalized dose to the out-of-beam portion of
each partially-irradiation organ to the in-beam portion (i.e. ). Note that the

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esophagus was not included in the Child model and the small intestine was fully-irradiated in the
Baby model..................................................................................................................................116
Table 7.4 Results of exponential regression analysis describing as a function of perimeter
(cm) for the in-beam segment of partially-irradiated organs. ......................................................118
Table 7.5 The percent coverage of each partially-irradiated organ for a typical abdomen scan to
each GSF patient model...............................................................................................................119
Table 7.6 The average percent coverage for a typical abdomens scan of each partially-irradiated
organ across patients (αorgan) and the corresponding standard deviation......................................119
Table 7.7 Estimated values obtained using Equation 7.9 for the partially-irradiated organs
of each GSF patient model...........................................................................................................120
Table 7.8 Percent errors of the estimates obtained with the method derived in this chapter
with respect to the simulated values obtained with simulation (Table 6.4). The average and
standard deviation of the absolute percent errors across patient models are in the last two
columns........................................................................................................................................120
Table 8.1 Results of the exponential regression analysis between from fixed tube current
scans and patient perimeter. For each organ the patient cohort, AO and BO coefficients, and
correlation coefficient (R2
) is reported.........................................................................................141
Table 8.2 Summary statistics for the percent errors of organ dose estimates calculated with
CTDIvol,Avg mAs with respect to doses obtained from TCM simulations, including: root mean
square, minimum error, and maximum error across patients in appropriate cohort. ...................145
Table 8.3 Results of the linear regression analysis between kP,O and patient perimeter. For each
organ the patient cohort, CO and DO coefficients, and correlation coefficient (R2
) is reported ...147
Table 8.4 Summary statistics for the leave-one-out cross-validation analysis to quantify the
percent errors for estimated doses calculated using CTDIvol,Ref mAs and kP,O from Equation 8.7. .148
Table 9.1 Theoretical HVL and QVL values for monoenergetic photon beams. ........................165
Table 9.2. Theoretical HVL and QVL values for polyenergetic photon beams. The kVp, tube
target material, and tube filtration material of the IEC beam quality reference spectrum is also
listed.............................................................................................................................................166
Table 9.3 Results of HVL and QVL simulations for monoenergetic beams including the energy,
air kerma with and without the Al filter, their ratio, and percent error from the theoretical ratio.
.....................................................................................................................................................166
Table 9.4 Results of HVL and QVL simulations for polyenergetic beams including the kVp, air
kerma with and without the Al filter, their ratio, and percent error from the theoretical ratio. ...166
Table 9.5 Design specifications of the virtual scanner as defined by AAPM Task Group 195...169

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Table 9.6 MCNPX simulated kerma for the axial segments of CTDI-like phantom from the
monoenergetic beam in keV/kg/NPS for the narrow and wide beam widths. .............................175
Table 9.7 MCNPX simulated kerma for the axial segments of CTDI-like phantom from the
polyenergetic beam in keV/kg/NPS for the narrow and wide beam widths. ...............................176
Table 9.8 MCNPX simulated kerma values for the center and peripheral CTDI rod-like volume
from each gantry angle in units of MeV/kG/NPS. The average kerma from angles 0 to 360 is
reported for the peripheral rod. ....................................................................................................178
Table 9.9 The percent error of each CTDI100,center and CTDI100,periphery simulation. ............187
Table 9.10 The percent error of each HVL and QVL simulation................................................187
Table 9.11 The Root Mean Square percent error of each bowtie profile simulation...................189
Table 9.12 The measured and simulated doses to the ionization chamber located on the surface of
the thorax phantom and the simulation percent error for each actual start angle.........................198

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Acknowledgments
First and foremost I thank Dr. Mike McNitt-Gray who has been a strong and
supportive advisor throughout my graduate career. The greatest professional decision I
made during my time in graduate school was to jump at the opportunity of joining Mike‘s
lab group. Over the last four years I underwent a transformation from a typical physics
student with the ability to learn out of a book and do homework problems to a scientist
whose main goal is the development of new knowledge based on critical and creative
thinking. I fully attribute that transformation to the influence Mike has had on me. There
is no way to adequately express my gratitude for the lessons he bestowed upon me in
areas of medical physics, the academic world, and life in general.
I also would not have gotten to the point I‘m at today without Dr. Chris Cagnon.
Chris‘ ability to frame my work in the perspective of reality always reminded me that the
research being done by our group was groundbreaking and state of the art. He reminded
me that while most diagnostic medical physicists are satisfied with ―being within a factor
of 2‖ it is up to us to try harder and to raise the bar. His enthusiasm was infectious and I
always walked away from our conversations with a renewed sense of confidence that my
work was important and worthwhile. I would like to thank Chris for his friendship over
the years. From day one he treated me as a colleague, rather than just as a student, and I
will always appreciate that.
I am also extremely grateful for the time and effort devoted to my work by Dr.
John DeMarco. As the resident Monte Carlo guru, it was a pleasure and a privilege to
learn the ins and outs of the MCNPX Monte Carlo code from John. His knowledge of the
intricate details that go into the physical models used for radiation transport was an
inspiration. I always prepared for research meetings or presentations with the expectation
that he would ask me a complex question, and I know that made me a better all around
researcher. As I head into a radiation oncology residency program, John‘s expertise,
dedication, and work ethic will be the example I strive to achieve.

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I am pleased to thank Dr. Matt Brown for sitting on my Ph.D. committee and for
being an excellent role model over the past four years. I had the privilege of interacting
with him on a regular basis during the weekly MedQIA research/journal clubs. His
lessons on how to properly design and execute a scientific study played a large role in
how I went about my dissertation work. Also, as the co-founder and Chief Scientific
Officer of MedQIA, I thank him for the office space in the company headquarters that I
used for four long years.
While I did not get to directly work alongside Dr. David Satlzberg, I‘d like to
thank him for sitting on my Ph.D. committee. His very helpful advice and insightful
questions during my first oral examination helped me to sharpen the focus of my
dissertation projects. I also owe him a huge thank you for agreeing to attend my doctoral
defense on the afternoon after undergoing surgery. Not many committee members would
do that, especially for a student they don‘t know extremely well.
I will also take this opportunity to thank the entire MedQIA staff, especially Dr.
Jonathan Goldin for serving as an exemplary academic physician and contributing to my
training on how to break down and scrutinize scientific publications, Richie Pais for his
computer programming expertise and always being around for a friendly conversation,
and Laura Guzman and Kimberly Easter for helping me with administrative and work
related issues. Also, I thank Terry More and Reth Thach for all the assistance they
provided me with student affairs and issues related to the Biomedical Physics
Department. It was a pleasure working with all of you over the years.
Any success that I‘ve had during graduate school can be directly attributed to my
labmates that worked with me side by side. First, I thank Dr. Erin Angel very much for
her patience with me in the early days when I averaged two to three questions a minute. I
am convinced that without her tutelage, advice, and procrastination sessions I would have
been lost from the start and never found my way as I did. I also express my sincere
gratitude to Maryam Khatonabadi. Her ability to catch on and quickly understand

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advanced concepts that were thrown at her always impressed me. I appreciate all the help
with the projects we collaborated on over the past two years. Finally, I owe Di Zhang one
of the biggest thanks of all. Di and I entered the lab group around the same time and I
always considered him more of a partner than just a labmate. Di always seemed to have
the answer when I had questions (and I had a lot of them), but even more importantly,
was always willing to drop what he was doing for an impromptu white board session or
code review. I can only hope I was able to contribute to all of his success as much as he
contributed to mine. I am very proud to have worked alongside these three individuals
and to be able to call them good friends.
I would have never made it through graduate school without the help of my
friends that were always there to help me forget I was in graduate school in the first
place. I am especially grateful to Gabe Marcus and Jeff Wright for being excellent
roommates, softball teammates, and drinking buddies. You guys were my Los Angeles
support system and I can‘t thank you enough. I also would like to thank my good friends
in Phoenix, AZ who were always ready for a fun time during my frequent weekend visits,
especially Greg McNamee, Megan McNamee, Heather Nystedt, Travis Harris, and Matt
Gioseffi.
My family has always been my main source of support, encouragement, and
motivation. I thank my father, Gary Turner, for teaching me integrity, honesty, hard
work, and kindness. To my mother, Marilynn Turner, I express enormous gratitude for
instilling in me the concepts of love, compassion, and respect. There is no way to
adequately pay back all they have given me, but as a start, I dedicate this dissertation to
them. I also thank my little brother Nathan. I am proud of his hard work at the University
of Arizona during my time in graduate school. I see nothing but success in his future as I
know he will continue to Bear Down. Finally, I sincerely thank Mark and Donna Hebein
for their support over the past few years. I am honored to be joining their family in a few
months and can‘t thank them enough for helping Jenna and I travel back and forth
between Phoenix and Los Angeles.

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I owe the biggest thank you to my fiancée Jenna Hebein. Since we met in
February of 2009 my life has had a true direction and purpose. Her undying support, even
during my most difficult periods of graduate school, gave me the extra motivation I
needed to succeed. I have had an amazing time exploring Los Angeles, Phoenix, Las
Vegas, and the various other cities we have visited together. I can‘t wait to begin our life
together in Tucson this summer and get married next fall. I am extremely thrilled and
tremendously excited to move on to the next stage with her as my partner. She has made
it all worth it and to her I say, I love you very much.

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I would like to acknowledge the following grants and fellowships for funding portions of
this work:
 UCLA Graduate Division Fellowship (2010-2011)
 National Institute of Biomedical Imaging and Bioengineering - R01 EB004898
(2007-2010)
 National Institute of Biomedical Imaging and Bioengineering – NIBIB Training
Grant T32EB002101 (2006-2007)
The following are chapter-specific acknowledgments:
 Chapter 4 is based on the research published in the journal Medical Physics:
A. C. Turner, D. Zhang, H. J. Kim, J. J. DeMarco, C. H. Cagnon, E. Angel, D. D.
Cody, D. M. Stevens, A. N. Primak, C. H. McCollough, and M. F. McNitt-
Gray, ―A method to generate equivalent energy spectra and filtration models
based on measurement for multidetector CT Monte Carlo dosimetry
simulations,‖ Med. Phys. 36(6), 2154–2164 (2009).
 Chapter 5 is based on research published in the journal Medical Physics and
presented at the Radiological Sciences of North America (RSNA) Annual Meeting in
Chicago, IL in December, 2008. This work was awarded the 2009 Norm Baily Award
from the Southern California Chapter of the American Association of Physicists in
Medicine (AAPM):
A. C. Turner, M. Zankl, J. J. DeMarco, C. H. Cagnon, D. Zhang, E. A. Angel, D. D.
Cody, D. M. Stevens, C. H. McCollough, and M. F. McNitt-Gray, ―The
feasibility of a scanner-independent technique to estimate organ dose from
MDCT scans: Using CTDIvol to account for differences between scanners,‖
Med. Phys. 37(4), 1816–1825 (2010).
A.C. Turner, E. Angel, D. Zhang, J.J. DeMarco, M. Zankl, M.F McNitt-Gray, C.H.
Cagnon, D.M. Stevens, A.N. Primak, D.D. Cody, and C.H. McCollough,
―Comparison of Organ Dose among 64 Detector MDCT Scanners from
Different Manufacturers: A Monte Carlo Simulation Study,‖ (abstr.) In:
Radiological Society of North America scientific assembly and annual meeting
program, Chicago, IL, SSJ23-03, 502 (2008).
 Chapter 6 is based on the research published in the journal Medical Physics and
presented at the Radiological Sciences of North America (RSNA) Annual Meeting in
Chicago, IL in December, 2009:

xviii
A.C. Turner, D. Zhang, M. Khatonabadi, M. Zankl, J.J. DeMarco, C.H. Cagnon, D.D.
Cody, D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility
of patient size-corrected, scanner-independent organ dose estimates for
abdominal CT exams,‖ Med. Phys. 38(2), 820-829 (2011).
A.C. Turner, M. Zankl, J.J. DeMarco, E. Angel, C.H. Cagnon, D. Zhang, and M.F.
McNitt-Gray, ―A Method to Estimate Organ Doses from Multidetector Row CT
Abdominal Exams from Patient Sized Corrected CT Dose Index Values: A
Monte Carlo Study,‖ (abstr.) In: Radiological Society of North America
scientific assembly and annual meeting program, Chicago, IL, SSG19-04, 472
(2009).
 Chapter 8 is based on the research presented at the Radiological Sciences of North
America (RSNA) Annual Meeting in Chicago, IL in December, 2010:
A.C. Turner, E. Angel, and M.F. McNitt-Gray, ―The Feasibility of Accounting for
Tube Current Modulation in Patient- and Scanner-Specific Organ Dose
Estimates from CT,‖ (abstr.) In: Radiological Society of North America
scientific assembly and annual meeting program, Chicago, IL, SSA20-04
(2010).
 Chapter 9 is partially based on the research presented and the research that will be
presented at the following scientific meetings:
I. Sechopoulos, S. Abboud, E. Ali, A. Badal, A. Badano, S.S.J. Feng, I. Kyprianou,
M. McNitt-Gray, E. Samei, and A.C. Turner, ―Introduction to the AAPM Task
Group No. 195 - Monte Carlo Reference Data Sets for Imaging Research,‖
(abstr.) In. American Associate of Physicists in Medicine 53rd Annual Meeting,
Vancouver, BC, WE-G-110-6 (2011).
A.C. Turner and M.F. McNitt-Gray, ―A Proposed Approach for Validating Monte
Carlo Computed Tomography dosimetry simulations,‖ Poster, In: The First
International Conference on Image Formation in X-Ray Computed
Tomography, Salt Lake City, UT (2010).
A.C. Turner, M. Zankl, E. Angel, and M.F. McNitt-Gray, ―Evaluation of Different
Benchmark Measurements for Validating Monte Carlo MDCT Source Models
Used in Estimating Radiation Dose,‖ (abstr.) Poster. In: American Association
of Physicists in Medicine 52nd
Annual Meeting, Philadelphia, PA, SU-GG-I-39,
3110 (2010).

xix
VITA
September 12, 1983 Born, Phoenix, Arizona
2005 AAPM Undergraduate Summer Fellow
Memorial Sloan Kettering Cancer Center
New York, New York
2006 B.S., Physics
University of Arizona
Tucson, Arizona
2007-09 Graduate Student Researcher
Los Angeles, California
2009 Norm Baily Award for Best Student Paper
Southern California Chapter of the AAPM
2010 Greenfield Award for Excellence in Medical Imaging
UCLA Biomedical Physics Interdepartmental Graduate Program

xx
PUBLICATIONS AND PRESENTATIONS
E. Angel, N. Yaghmai, H. Kim, J. Demarco, C. Cagnon, A. Turner, D. Zhang, J. Goldin,
and M. McNitt-Gray, ―How Well Does CTDI Estimate Organ Dose to Patients From
Multidetector (MDCT) Imaging?,‖ oral presentation. (abstr.) In: American Association
of Physicists in Medicine 50th
Annual Meeting, Houston, TX, WE-D-332-03, (2008).
M. Khatonabadi, M.F. McNitt-Gray, A.C. Turner, D. Zhang, E. Angel, T. Hall, and I.
Boechat, ―The Effects of Incorrect Choice of Patient Size References (Adult/Child) On
Tube Current Modulation,‖ oral presentation. (abstr.) In: American Association of
Physicists in Medicine 52nd
Annual Meeting, Philadelphia, PA, MO-EE-A4-03, 3351
(2010).
M. Khatonabadi, E. Angel, M.F. McNitt-Gray, A.C. Turner, and D. Zhang, ―The
Accuracy of Organ Doses Estimated from Monte Carlo CT Simulations Utilizing
Approximations to the Tube Current Modulation Function,‖ oral presentation. (abstr.)
In: Radiological Society of North America scientific assembly and annual meeting
program, Chicago, IL, SSA20-01 (2010).
M. Khatonabadi, M.F. McNitt-Gray, E. Angel, A.C. Turner, and D. Zhang, ―The Effect
of Incorrect Selection of Reference Patient Size (Adult/Child) When Using Tube
Current Modulation (TCM) in CT,‖ oral presentation. oral presentation. (abstr.) In:
K. Mathieu, A. Turner, C. Cagnon, and D. Cody, ―kVp modulation schemes designed to
reduce breast dose,‖ oral presentation. (abstr.) In: Radiological Society of North
America scientific assembly and annual meeting program, Chicago, IL, SSA20-03
(2010).
M.F. McNitt-Gray, E. Angel, A.C. Turner, D.M. Stevens, A.N. Primak, C.H. Cagnon, et
al. ―CTDI Normalized to Measured Beam Width as an Accurate Predictor of Dose
Variations for Multidetector Row CT (MDCT) Scanners Across all Manufacturers,‖
oral presentation. (abstr.) In: Radiological Society of North America scientific
assembly and annual meeting program, Chicago, IL, SSJ23-04, 502 (2008).
M.F. McNitt-Gray, J.J. DeMarco, C.H. Cagnon, A.C. Turner, and D. Zhang, ―Monte-
Carlo Simulation Approach to Estimating Patient Radiation Dose from MDCT
Exams,‖ oral presentation. The First International Conference on Image Formation in
X-Ray Computed Tomography, Salt Lake City, UT (2010).
C. Morioka, A. Turner, M. McNitt-Gray, F. Meng, M. Zankl, and S. El-Saden,
―Development of a DICOM Structure Report to Track Patient‘s Radiation Dose to
Organs from Abdominal CT Exams,‖ poster presentation. American Medical
Informatics Association annual meeting, Washington D.C., (2010).

xxi
A.D. Sodickson, A.C. Turner, K. McGlamery, and M.F. McNitt-Gray, ―Variation in
Organ Dose from Abdomen Pelvis CT Exams Performed with Tube Current
Modulation (TCM): Evaluation of Patient Size Effects,‖ oral presentation. (abstr.) In:
A.C. Turner, C.J. Watchman, and R.J. Hamilton, "Probabilistic Analysis of
Radiation Induced Pneumonitis as a Function of Tumor and Margin Size,"
poster presentation. Int. Jour. Rad. Onc. Biol. Phys. Vol. 66 No. 3 Supplement
2006.
A.C. Turner, E. Angel, D. Zhang, J.J. DeMarco, C.H. Cagnon, and M.F. McNitt-Gray,
―The Relationship between Half Value Layer (HVL) and CTDI for Multidetector CT
(MDCT),‖ poster presentation. American Association of Physicists in Medicine 50th
Annual Meeting, Houston, TX, SU-GG-I-62 (2008).
A.C. Turner, E. Angel, D. Zhang, J.J. DeMarco, M. Zankl, M.F McNitt-Gray, C.H.
Cagnon, D.M. Stevens, A.N. Primak, D.D. Cody, and C.H. McCollough, ―Comparison
of Organ Dose among 64 Detector MDCT Scanners from Different Manufacturers: A
Monte Carlo Simulation Study,‖ oral presentation. (abstr.) In: Radiological Society of
North America scientific assembly and annual meeting program, Chicago, IL, SSJ23-
03, 502 (2008).
A.C. Turner, D. Zhang, H.J. Kim, J.J. DeMarco, C.H. Cagnon, E. Angel, D.D. Cody,
D.M. Stevens, A.N. Primak, C.H. McCollough, and M.F. McNitt-Gray, ―A method to
generate equivalent energy spectra and filtration models based on measurement for
multidetector CT Monte Carlo dosimetry simulations,‖ Med. Phys. 36(6), 2154-2164
(2009).
A.C. Turner, M. Zankl, E. Angel, and M.F. McNitt-Gray, ―Comparisons of Organ and
Effective Doses from ImPACT and DLP ED Methods to MDCT Specific Monte Carlo
Simulations,‖ poster presentation. American Association of Physicists in Medicine 51st
Annual Meeting, Anaheim, CA, SU-FF-I-53 (2009).
A.C. Turner, M. Zankl, J.J. DeMarco, E. Angel, C.H. Cagnon, D. Zhang, and M.F.
McNitt-Gray, ―A Method to Estimate Organ Doses from Multidetector Row CT
Abdominal Exams from Patient Sized Corrected CT Dose Index Values: A Monte
Carlo Study,‖ oral presentation. (abstr.) In: Radiological Society of North America
scientific assembly and annual meeting program, Chicago, IL, SSG19-04, 472 (2009).
A.C. Turner, M. Zankl, J.J. DeMarco, C.H. Cagnon, D. Zhang, E. Angel, D.D. Cody,
D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility of a
scanner-independent technique to estimate organ dose from MDCT scans: using
CTDIvol to account for differences between scanners,‖ Med. Phys. 37(4), 1816-1825
(2010).

xxii
A.C. Turner and M.F. McNitt-Gray, ―Scanner- and Patient-Specific Multidetector CT
Organ Dose Estimates from CTDI and Patient Size Measurements,‖ oral presentation.
The First International Conference on Image Formation in X-Ray Computed
Tomography, Salt Lake City, UT (2010).
A.C. Turner and M.F. McNitt-Gray, ―A Proposed Approach for Validating Monte Carlo
Computed Tomography dosimetry simulations,‖ poster presentation. The First
International Conference on Image Formation in X-Ray Computed Tomography, Salt
Lake City, UT (2010).
A.C. Turner and M.F. McNitt-Gray, ―Scanner-and Patient-Specific Multidetector CT
Organ Dose Estimates from CTDI and Patient Size Measurements,‖ poster
presentation. National Institute of Biomedical Imaging and Bioengineering Training
Grant Meeting, Bethesda, MD (2010).
A.C. Turner, M. Zankl, E. Angel, and M.F. McNitt-Gray, ―Evaluation of Different
Benchmark Measurements for Validating Monte Carlo MDCT Source Models Used in
Estimating Radiation Dose,‖ poster presentation. (abstr.) In: American Association of
Physicists in Medicine 52nd
Annual Meeting, Philadelphia, PA, SU-GG-I-39, 3110
(2010).
A.C. Turner, E. Angel, and M.F. McNitt-Gray, ―The Feasibility of Accounting for Tube
Current Modulation in Patient- and Scanner-Specific Organ Dose Estimates from CT,‖
assembly and annual meeting program, Chicago, IL, SSA20-04 (2010).
A.C. Turner, D. Zhang, M. Khatonabadi, M. Zankl, J.J. DeMarco, C.H. Cagnon, D.D.
Cody, D.M. Stevens, C.H. McCollough, and M.F. McNitt-Gray, ―The feasibility of
patient size-corrected, scanner-independent organ dose estimates for abdominal CT
exams,‖ Med. Phys. 38(2), 820–829 (2011).
D. Zhang, M. Zankl, J.J. DeMarco, C.H. Cagnon, E. Angel, A.C. Turner, and M.F.
McNitt-Gray, ―Reducing radiation dose to selected organs by selecting the tube start
angle in MDCT helical scans: a Monte Carlo based study,‖ Med. Phys. 36(12), 5654-
64 (2009).
D. Zhang, A.S. Savandi, J.J. DeMarco, C.H. Cagnon, E. Angel, A.C. Turner, D.D. Cody,
D.M. Stevens, A.N. Primak, C.H. McCollough, and M.F. McNitt-Gray, ―Variability of
surface and center position radiation dose in MDCT: Monte Carlo simulations using
CTDI and anthropomorphic phantoms,‖ Med. Phys. 36(3), 1025-1038 (2009).
D. Zhang, J.J. DeMarco, C.H. Cagnon, E. Angel, A.C. Turner, M. Zankl, and M.F.
McNitt-Gray, ―Reducing Dose to a Small Organ by Varying the Tube Start Angle in a
Helical CT Scan,‖ oral presentation. (abstr.) In: American Association of Physicists in
Medicine 51st
Annual Meeting, Anaheim, CA, TU-C-304A-06, 2728 (2009).

xxiii
D. Zhang, A.C. Turner, C.H. Cagnon, J.J. DeMarco, and M.F. McNitt-Gray MF, ―Dose
from CT Brain Perfusion Examinations: a Monte-Carlo Study to Look into
Deterministic Effects,‖ oral presentation. The First International Conference on Image
Formation in X-Ray Computed Tomography, Salt Lake City, UT (2010).
D. Zhang, C.H. Cagnon, J.J. DeMarco, A.C. Turner, and M.F. McNitt-Gray, ―Novel
Strategies to Reduce Patient Organ Dose in CT without Reducing Tube Output,‖
poster presentation. The First International Conference on Image Formation in X-Ray
Computed Tomography, Salt Lake City, UT (2010).
D. Zhang, C.H. Cagnon, J.J. DeMarco, M. Zankl, A.C. Turner, M. Khatonabadi, and
M.F. McNitt-Gray, ―Estimating Dose to Eye Lens and Skin From Radiation Dose
From CT Brain Perfusion Examinations: Comparison to CTDIvol Values,‖ oral
presentation. (abstr.) In: American Association of Physicists in Medicine 52nd
Annual
Meeting, Philadelphia, PA, TU-A-201B-4, 3373 (2010).
D. Zhang, C.H. Cagnon, J.J. DeMarco, M. Zankl, A.C. Turner, M. Khatonabadi, and
M.F. McNitt-Gray, ―Reducing Eye Lens Dose During Brain Perfusion CT
Examinations by Moving the Scan Location or Tilting the Gantry Angle,‖ poster
presentation. (abstr.) In: American Association of Physicists in Medicine 52nd
Annual
Meeting, Philadelphia, PA, SU-GG-I-37, 3109 (2010).
D. Zhang, C.H. Cagnon, J.J. DeMarco, C.H. McCollough, D. Cody, M.F. McNitt-Gray,
A.C. Turner, and M. Khatonabadi, ―Estimating Radiation Dose to Eye Lens and Skin
from CT Brain Perfusion Examinations: A Monte Carlo Study,‖ oral presentation.
(abstr.) In: Radiological Society of North America scientific assembly and annual
meeting program, Chicago, IL, SSG14-01 (2010).
D. Zhang, C.H. Cagnon, J.J. DeMarco, C.H. McCollough, D. Cody, M.F. McNitt-Gray,
M. Zankl, A.C. Turner, and M. Khatonabadi, ―How Do CTDI and TG111 Small
Chamber Dose Perform in Estimating Radiation Dose to Eye Lens and Skin from CT
Brain Perfusion Examinations for Patients with Various Sizes: A Monte Carlo Study,‖
assembly and annual meeting program, Chicago, IL, SSM20-02 (2010).

xxiv
ABSTRACT OF THE DISSERTATION
The Development of Methods to Assess Radiation Dose to Organs
from Multidetector Computed Tomography Exams
Based on Detailed Monte Carlo Dosimetry Simulations
By
Doctor of Philosophy in Biomedical Physics
University of California, Los Angeles, 2011
Professor Michael McNitt-Gray, Chair
Computed Tomography (CT) has become an extremely valuable diagnostic
imaging modality, however, its widespread utilization has lead to a considerable increase
in its contribution to the collective radiation dose from medical procedures. It has been
suggested that the most appropriate quantity for assessing the risk of carcinogenesis from
diagnostic imaging procedures is the radiation dose to individual organs. The current
paradigm to assess dose from CT exams (i.e. the CT Dose Index) involves measuring

xxv
dose to homogenous, cylindrical phantoms and therefore does not directly quantify the
dose to any particular patient or organ. The overall goal of the work presented in this
dissertation is to develop a comprehensive methodology to accurately estimate the
radiation dose absorbed by individual organs in patients undergoing CT examinations.
In this dissertation, a Monte Carlo based modeling package that simulated the
delivery of radiation from modern multidetector CT (MDCT) scanners was used to
determine the radiation dose to organs segmented in detailed patient models. In order to
simulate the x-ray source characteristics from any MDCT scanner, the validity of a
method to generate a photon energy spectrum and filtration description (including the
bowtie filter) based only on scanner-specific measurements was demonstrated.
The range of doses from different scanners was investigated by obtaining organ
doses to a single patient model with Monte Carlo simulations for a range of patients from
MDCT scanners from the four major scanner manufacturers. This work revealed that
there is considerable variation across scanners in both CTDIvol and organ dose values.
However, because these variations are similar, the difference of organ doses normalized
by CTDIvol across scanners is considerably smaller. This confirms that, for a given
patient, it is possible to generate a set of organ-specific, scanner-independent CTDIvol-to-
organ dose conversion coefficients.
The influence of patient size was investigated by performing Monte Carlo
simulations using a cohort of eight patient models including both genders and that ranged
in size from infant to large adult. This work revealed that for fully-irradiated organs,

xxvi
CTDIvol-to-organ dose conversion coefficients have a strong decreasing exponential
correlation with patient perimeter. The doses to organs completely outside the scan were
essentially negligible. A follow up study revealed that CTDIvol-to-organ dose conversion
coefficients for organs partially-irradiated can also be predicted based on patient
perimeter and an estimate of the percent of the organ included in the scan region.
Additionally, it was shown that the dose reduction effects of tube current modulation
(TCM) can be taken into account based on patient-specific correction factors.
This work demonstrated the feasibility of a comprehensive methodology to
estimate organ dose to patients undergoing CT exams. This method results in patient-and
exam-specific CTDIvol-to-organ dose conversion coefficients that can be used with the
CTDIvol reported by the scanner to calculate absolute dose values. In conclusion, it is
possible to obtain accurate estimates of organ dose to any patient from any scanner,
which represents a significant improvement over current conventional CT dosimetry
practices.

1
Chapter 1 Background and Motivation
X-ray computed tomography (CT) has become an integral diagnostic imaging
modality and is now routinely used within many areas of the medical community. The
use of CT has become the preferred alternative to traditional two-dimensional projection-
based imaging (such as radiography) for a large number of applications because of its
ability to distinguish between overlapping structures that would otherwise be subject to
superposition in the final image.1
Additionally, CT scanners employ a geometry and
filtration design that limits the detection of scattered photons, resulting in its inherently
high contrast resolution.1
In addition to these advantageous, the excellent isotropic spatial
resolution and image quality of modern scanners makes CT an excellent modality for
diagnosing tumors, calcifications, etc. and is regularly used to study structures in the
head, chest, abdomen, and pelvis.
Figure 1.1 CT images of various anatomical regions including A) abdomen, B) chest, C)
head.
The fundamental principles of CT are based on the concept of the Radon
transform: that it is possible to produce a two-dimensional image of an unknown object
from series of one-dimensional projections through that object.1
During a CT exam,

2
projections are obtained by rotating an x-ray source around a patient and continuously
detecting the portion of the radiation that is not attenuated. A simple diagram of a third-
generation CT scanner is shown in Figure 1.2.A. The patient lies on a bed that moves
either incrementally (axial CT) or continuously (helical CT) as the radiation source
rotates (Figure 1.2.B illustrates the source motion of a helical scan). Modern CT scanners
use fan-beams and multiple rows of solid-state detectors (multidetector row CT or
MDCT) to measure the individual x-ray projections from each source position.
Computers are then used to reconstruct multiple two-dimensional axial images, typically
through filtered backprojection algorithms. The final images represent maps of material-
specific mass attenuation coefficients, and thus display detailed representations of the
patient‘s anatomy.
Figure 1.2 A) Diagram of a third-generation CT scanner including the rotating x-ray
source, rotating detector array, and the translating table. B) Illustration of the x-ray source
path for a helical CT scan1.

3
The radiation used for CT exams is generated by x-ray tubes that accelerate
electrons produced by thermionic emission from a filament heated by an electric current
(the cathode) towards a tungsten anode with tube voltages that range from 80 to 140 kV.
Accelerated electrons interact with the tungsten anode causing them to slow down and
emit bremsstrahlung photons with an energy range from ~0 keV up to the peak
kilovoltage (kVp) of the x-ray tube (i.e. 80-140 keV). Low energy photons are typically
reabsorbed by the tungsten anode. Additionally, the tungsten atoms can be ionized due to
electrostatic forces resulting in inner-shell vacancies and, subsequently, characteristic x-
ray emission. A typical tungsten anode spectrum is shown in Figure 1.3.
Figure 1.3 Probability distribution function (PDF) of a typical tungsten anode x-ray tube
spectrum for a tube voltage of 140 kV (i.e. 140 kVp) used for CT scanners.
The fluence of photons in the beam is a function of two factors: a) the kVp and b)
the tube current time product. The ratio of the fluence between two different kVp values
is proportional to the square of the ratios of the kVp values. The fluence is linearly
proportional to the product of the current passing through the cathode filament (mA) and

4
the time the current is applied (s), which is denoted the tube current time product and has
units of mAs. CT x-ray tubes employ filtration material to harden the beam in order to
reduce the number of low energy photons that would have no chance to pass through the
patient. Additionally, specially shaped filters, called bowtie filters, are used to shape the
fan-beam so that more photons pass through the thicker portion of the patient relative to
the thinner part of the patient (this ensures a more even fluence distribution at the
detectors).
1.1 Radiation Risks from CT Exams
CT exams expose patients to ionizing x-ray radiation and therefore result in a
non-trivial increase in the risk of carcinogenesis in adults and particularly in children2-7
The absorbed dose is the metric used to quantify the amount of energy imparted to a
patient or phantom (in Joules) per unit mass (in kilograms). 3,4
The unit for absorbed dose,
or just dose, is the Gray, where 1 Gray = 1 J/kg. While the doses associated with CT are
typically not large enough to result in immediate cell death, the x-rays are energetic
enough to ionize atoms via photoelectric or Compton scattering interactions.6
This
ionization process can lead to DNA strand breaks or base pair damages, either by the
direct ionization of DNA atoms or, more commonly, from the interaction of DNA with
nearby ionized atoms (most notably hydroxyl radicals resulting from ionized water
molecules). Cellular repair mechanisms are usually able to either correctly repair single
or double strand breaks or initiate apoptosis, however, it is possible that DNA will be
repaired incorrectly but the cell will continue to proliferate despite genetic mutations.

5
This results in subsequent replication of incorrect DNA and this is the basic mechanism
for carcinogenesis.
The accurate quantification of the relatively small risks associated with the dose
levels typical of CT exams through epidemiological studies is difficult due to the large
number of subjects required to derive meaningful statistics.7
The most widely studied
cohort of patients for radiation-induced cancer is the survivors of the atomic bombs
dropped on Japan in 1945. It should be noted that these subjects received a single dose of
whole-body radiation which differs from the heterogeneous dose distributions delivered
by individual CT exams. Despite these differences, studies of the atomic bomb survivors
have shown that there is a statistically significant increased risk of carcinogenesis from
the radiation dose levels associated with CT exams and that this risk decreases with age
(less time for cancer to manifest).6
More importantly, it has been shown that the most
appropriate metric for assessing the risk due to diagnostic imaging procedures is the
radiation dose to individual organs.2-6
Conversion factors to calculate the probability of
cancer induction or mortality based on organ doses have been published in the National
Research Council‘s report on the Biological Effects of Ionizing Radiation (BEIR VII –
Phase 2, Tables 12D-1 and 12D-2) for a number of different radiosensitive organs in
males and females with ages ranging from 0 to 80 years.2
Recent studies report that from 1993 to 2006 the number of CT imaging
procedures increased at an annual rate of over 10% in the United States, leading to a
considerable increase in the collective radiation dose from CT.8
Specifically, CT exams

6
now constitute 15% of the total number of radiological imaging procedures, but
contribute more than 50% of the population‘s medical radiation exposure.8
Typically,
patients only receive a single exam, however, some individuals, such as those being
treated for cancer, can receive multiple scans in a short period of time. Regardless,
because the risks associated with CT scans are stochastic in nature and there is no known
threshold dose for carcinogenesis, it is imperative to ensure that the benefits of every CT
scan outweigh the risk. These concerns suggest that it is necessary to properly assess and
monitor the radiation doses being delivered to patients from CT, specifically, the
radiation doses to individual organs.
1.2 Routine Clinical CT Dosimetry Assessment (CTDI and DLP)
The CT dose index (CTDI), introduced by Shope et al.9
in 1991, has become the
standard metric for measuring the radiation dose from a multiple detector row CT
(MDCT) scan.3,10,11
The CTDI is defined as the average dose in the longitudinal center of
a cylindrical phantom from a contiguous axial exam with a scan length much greater than
the width of the x-ray beam. The average dose from a contiguous axial exam to a
cylindrical slab at center of the phantom with a thickness equal to the beam width
(denoted multiple scan average dose or MSAD) is given by:
Eq. 1.1

7
where D(z) is the total dose profile (dose envelope in Figure 1.4), which is the sum of the
dose profiles from each individual rotation, and I is the width of the beam. Shope et al.
demonstrated that, when the distance between each consecutive tube rotation is the same
as the width of the beam, the integral in Equation 1.1 is equivalent to the infinite integral
of the dose profile from a single rotation .9
The beam width for multidetector CT
(MDCT) scanner is the product of the number of detector rows (N) and the width of each
detector (T), so the CTDI is defined as:
Eq. 1.2
where Dsingle(z) is the dose profile along the longitudinal (z) axis from a single axial scan
(single rotation with no table movement).
Figure 1.4 The longitudinal dose profile from a contiguous axial exam. The profile for each
rotation and the summation is shown. Reprinted from C.H. McCollough, et al.11

8
Based on its definition, the CTDI is a theoretical value that cannot be directly
obtained since it is not possible to measure the infinite dose profile. However, since the
dose profile for a single rotation scan for 64-slice MDCT scanners approaches zero when
z=±50 mm, even for the widest collimations, the CTDI can be closely approximated by
measuring the exposure with a 100 mm pencil ionization chamber and electrometer and
then converting to dose.10
This is the fundamental CTDI measurement, denoted CTDI100,
and is described by Equation 1.3:
Eq. 1.3
where f is the conversion factor from exposure to a dose in air (0.87 rad/R), C is the
calibration factor for the electrometer, E is the measured value of exposure in Roentgens
and L is the active length of the ionization chamber (100 mm).
CTDI phantoms are homogenous and constructed of polymethyl methacrylate
(PMMA). Standard CTDI phantoms come in two sizes, a 16 cm diameter ―head‖
phantom and a 32 cm diameter ―body‖ phantom. Head and body CTDI phantoms come
with pre-drilled holes along the longitudinal axis that accept either the pencil ionization
chamber or a PMMA insert, with one hole along the axial center and four along
peripheral positions, as shown in Figure 1.5. The phantoms are positioned with the
central hole at the scanner isocenter and the peripheral holes at 0, 90, 180, and 270
degrees in the gantry. CTDI100 values can be calculated with exposure values measured in
either the center (CTDI100,center) or any of the periphery holes (CTDI100,periphery).

9
Figure 1.5 16 cm diameter “head” and 32 cm diameter “body” CTDI phantoms composed
of PMMA and containing pre-drilled holes at center and four periphery positions.
There are several variants of the CTDI metric that are meant to account for the
heterogeneous dose distributions from CT scans.3,10
The weighted CTDI (CTDIW)
represents the weighted average of the dose at the center and periphery for the central
axial plane of the phantom, as is defined as:
. Eq. 1.4
The volume CTDI (CTDIvol) was defined to account for the dose from non-contiguous
scans, such as helical scans with a pitch not equal to 1, and is defined as:
Eq. 1.5
where pitch is the table movement for each rotation divided by the nominal collimation
(NT). All major scanner manufacturers report the CTDIvol for each scan in a particular
exam on their 64-slice MDCT scanner models. CT dose reports also commonly include
the Dose Length Product (DLP) for the exam, where DLP is defined as:

10
Eq. 1.6
1.3. Limitations of the CTDI
The measurement techniques used to obtain exposure values required to calculate
the CTDI100 and, subsequently, the other CTDI metrics are based on the assumption that
the 100 mm ionization chamber are sufficient for detecting the entire longitudinal beam
profile. As discussed above, this assumption is suitable for 64-slice MDCT scanners
which maximum longitudinal beam widths of 40 mm. Recently, commercial cone beam
CT (CBCT) systems with beam widths wide enough to cover a significant anatomical
length (50-160 mm) in a single axial rotation (e.g., for cardiac CT) have been developed
and are rapidly proliferating in the clinic. The larger beam widths employed by these
CBCT scanners result in significant scatter tails scatter tails (and in some cases, primary
radiation) well outside the detection range of a 100 mm ionization chamber, thus routine
CTDI measurement techniques are not adequate for assessing CBCT dose.12
To address this problem, the American Association of Physicists in Medicine
(AAPM) Task Group 111 has described a new paradigm for assessing CT dose.13
For CT
protocols that involve table translation it is still necessary to measure the dose profile
integral. According to the Task Group 111 report, this measurement should be obtained
by performing the prescribed scan and measuring exposure using a small volume
ionization chamber or a calibrated solid state detector centered in a 45 cm long PMMA
cylindrical phantom.13
AAPM Task Group 200 is currently producing a report to

11
standardize the implementation of this measurement, including the specifics of a new CT
dosimetry phantom.
It is very important to emphasize that both CTDI and AAPM Task Group 111-
type metrics are specifically defined to quantify the dose to simple, homogenous
phantoms. Despite the fact that these metrics are (and will remain) the most common
clinical measurement techniques to assess CT dose and are typically included in patient
dose reports, these values are not meant to be interpreted as actual dose to a particular
patient, or more specifically, to any particular organ.14
The sizes, shapes, and material
compositions of actual patients are considerably different than cylindrical PMMA CTDI
phantoms and only recently has there been an attempt to correct CTDI values for patient
size. AAPM Task Group 204 is currently developing correction factors which are
functions of both age and patient dimensions that can be used to convert CTDIvol values
for 32 and 16 cm diameter PMMA phantoms to pediatric scale water-equivalent doses15
.
Part of Task Group 204‘s results will be based on the results presented in Chapter 6 of
this dissertation.
Instead, the CTDI should be regarded as an index of a scanner‘s radiation output.
As a result, it is a useful tool for dose comparisons between different CT scan protocols
or scanner designs.16
1.4 Effective Dose from CT Exams and its Limitations

12
Effective dose (ED) was introduced as a health physics concept by the
International Commission on Radiation Protection (ICRP) to account for the various
radiosensitivities of the tissues that absorb energy from radiation.2-5,10,11
This quantity is
defined as an estimate of the whole-body radiation dose that would result in an equivalent
stochastic risk as the partial-body imaging procedure, and is mathematically defined as a
weighted average of the dose to several radiosensitive tissues (DT):
Eq. 1.7
where ωT is a tissue-specific radiosensitivity factor whose value is specified by the ICRP
based on epidemiological studies (the ICRP Publication 103 tissue weighting factors5
are
listed in Table 1.1) and ωR is a radiation weighting factor that account for the relative
biological damage imparted from the energy deposition of different types of particles (ωR
for photons is equal to 1. Effective dose is measured in units denoted Sieverts (Sv).
Table 1.1 ICRP Publication 103 recommended tissue weighting factors.5
Tissue ωT Σ ωT
Bone-marrow (red), Colon, Lung, Stomach, Breast, Remainder tissues* 0.12 0.72
Gonads 0.08 0.08
Bladder, Esophagus, Liver, Thyroid 0.04 0.16
Bone Surface, Brain, Salivary glands, Skin 0.01 0.04
Total 1.00
* Remainder tissues: Adrenals, Extrathoracic region, Gall bladder, Heart, Kidneys, Lymphatic
nodes, Muscle, Oral mucosa, Pancreas, Prostate (♂), Small intestine, Spleen, Thymus,
Uterus/cervix (♀).
A method to convert DLP values from CT scans to effective dose using anatomic
region-specific conversion factors (k-factors) was summarized in a report by the AAPM
Task Group 23.3,17,18
The k-factors are listed in Table 1.2. Originally, these k-factors were

13
only derived for a single geometrical patient model, namely the MIRD phantom meant to
represent the ―standard man‖. Despite subsequent work to adapt the factors for different
age groups and patient size ranges, effective dose estimates from k-factors do not take
patient-specific sizes or body habitus into account and therefore are only rough estimates.
It should be noted that effective doses provide only an approximate estimate of the true
risk. As stated above, doses to individual organs is the preferred quantity for optimal risk.
Table 1.2 Normalized effective dose per dose-length product (DLP) for adults (standard
physique) and pediatric patients of various ages over various body regions. Conversion
factor for adult head and neck and pediatric patients assume use of the head CT dose
phantom (16 cm). All other conversion factors assume use of the 32-cm diameter CT body
phantom3
Body Region
k (mSv mGy-1
cm-1
)
0 year old 1 year old 5 year old 10 year old Adult
Head and neck 0.013 0.0085 0.0057 0.0042 0.0031
Head 0.011 0.0067 0.0040 0.0032 0.0021
Neck 0.017 0.012 0.011 0.0079 0.0059
Chest 0.039 0.026 0.018 0.013 0.014
Abdomen ≈&Pelvis 0.049 0.030 0.020 0.015 0.015
Trunk 0.044 0.028 0.019 0.014 0.015
1.5. Existing Organ Dose Estimation Methods
In order to address the limitations of the CTDI, several techniques to quantify
organ doses have been reported. These methods typically involve either (a) physical
measurements in anthropomorphic phantoms or (b) simulations using computational
patient models. There are advantageous and disadvantages to each of these types of
studies.
1.5.1. Physical Phantom Studies

14
Physical dosimetry measurements allow the actual CT scanners and scanning
protocols of interest to be directly evaluated with detectors such as ionization chambers,
Thermoluminescence Detectors (TLD), Metal Oxide-silicon Semiconductor Field Effect
Transistor (MOSFET) detectors, or Optically Simulated Luminescence (OSL) detectors.
The majority of studies employ anthropomorphic phantoms with tissue-equivalent
materials to model the attenuation properties of actual patients.19-26
A number of these
types of phantoms are commercially available in different sizes to model various age
groups and they allow detectors to be placed inside in order to measure point doses.27
There are a number of limitations for studies that use physical measurements to
represent organ doses from CT exams. First, the available anthropomorphic phantoms do
not adequately represent the considerable variations in patient size, habitus, and
composition seen in actual patients (e.g. there is only one adult male sized phantom).
Also, the axial and longitudinal dose distributions from CT exams, especially at the
surface of patients, have considerable variability due to the helical path of the CT source
around the patient (Zhang showed variations up to 50% at the surface of
anthropomorphic phantoms when pitch is 1.5).28
Thus it is not valid to assume that a
point dose measurement within an organ is representative of the actual dose to the entire
organ volume.
Even more important is that, except for air ionization chambers, the majority of
detectors used in physical phantom studies exhibit a significant dependence on energy at
the relatively low x-ray energies of diagnostic imaging. CT x-ray beams are characterized

15
by distinct energy spectra shapes and high fluence. These factors make it difficult to
properly calibrate the energy dependent response of thermoluminescent detectors
(TLD‘s), optically stimulated luminescence detectors (OSL‘s), metal–oxide–
semiconductor field-effect transistor (MOSFET‘s), or other solid state-type detectors.
Making this problem even worse is that that the shape of the energy spectrum changes as
the beam is attenuated so a calibration factor obtained in air may be even worse for
measuring dose in phantom. Ionization chambers do not have a significant energy
dependence, however, they are difficult to imbed in a phantom because they are relatively
large and require an electrical connection to an electrometer.
1.5.2. Monte Carlo Dosimetry Simulations
The use of Monte Carlo radiation transport codes in computer packages that
simulate the delivery of radiation from CT scanners to patient models has become a
popular method of investigating organ dose.29-37
Typically, these codes take into account
scanner-specific characteristics such as x-ray energy spectra, filtration designs, beam
collimation, fan-angle, and pitch. Conventional Monte Carlo radiation transport
techniques are used to track the path of simulated photons through a computational
anthropomorphic phantom and tally the dose deposited in regions of interest.
The Monte Carlo simulation approach was used in the early 1990‘s for dosimetry
studies of single detector row, non-helical CT scanners performed by both the National
Radiation Protection Board (NRPB, Chilton, U.K.)29
and the GSF (National Research
Center for Environment and Health, Institute of Radiation Protection, Neuherberg,

16
Germany)30
. These initial studies simulated dose to the organs in very crude
mathematical phantoms meant to represent the standard human, such as the
hermaphroditic MIRD mathematical phantom (Figure 1.6.A). The organ dose results
reported by the NRPB have been incorporated into the widely used ImPACT CT Patient
Dosimetry Calculator (ImPACT, London, England).38
Methods to extend the results to
current, commercially available helical CT scanners have been developed, for example,
by matching new scanners to those originally simulated based on physical measurements
(such as CTDI). While these methods exist to estimate organ dose, differences between
the NRPB mathematical phantoms and actual patient models as well as inaccuracies
resulting from approximating doses to helical scanners from axial scanners using scanner
matching techniques may result in inaccurate dose estimates.
Figure 1.6 A) Screen shot from the ImPACT Dosimetry Calculator showing the MIRD
mathematical phantom used by the NRPB Monte Carlo Study. B) Adult females from GSF
Family of Voxelized Models.

17
Since these initial studies, a number of different techniques have been employed
by different research groups In order to develop detailed Monte Carlo CT dosimetry
packages that model specific multidetector CT (MDCT) scanners.31-37
These modern
codes typically utilize voxelized patient models that feature detailed organ definitions,
typically generated directly from patient images, either by manual segmentation or by
threshold algorithms based on CT numbers. Examples of detailed voxelized models, the
adult females from the GSF Family of Voxliezed Phantoms39-41
, are shown in Figure
1.6.B. The disparities between the different packages range from fundamental radiation
transport techniques to advanced aspects of modeling MDCT scanners. For example, it is
common to base simulation packages on well-validated, general purpose radiation
transport codes such as the Monte Carlo N-Particle (MCNP) code from Los Alamos
National Laboratory (e.g. the UCLA CT Dose Group31,42
); however, some groups have
created radiation transport code from scratch37
. Also, the methods used to model the
delivery of radiation from CT scanners can be quite different. On an even higher level,
the data sets used to simulate a specific scanner, such as x-ray energy spectrum or
filtration design, can vary across different codes.
CT organ dose studies based on Monte Carlo methods address many of the
limitations of physical measurement studies and have the potential to report very accurate
organ dose results from a wide range of CT scanners and protocols. However, it is
necessary to adequately validate the accuracy of simulations designed to model the dose
from particular CT scanners to specific patient models. Most Monte Carlo modeling

18
publications include descriptions of benchmark experiments carried out to validate the
code. Commonly, simple phantom measurements limited to simple homogeneous
cylindrical objects (such as CTDI) are compared to analogous simulations.
1.6. Discussion
In conclusion, it is clear that while CT is an extremely beneficial and widely used
diagnostic imaging modality it has introduced a non-trivial risk of carcinogenesis to the
population. The current state of CT dosimetry involves measuring the dose to two
different sized homogenous, cylindrical reference phantoms (CTDI with head and body
phantoms) and therefore does not directly assess patient dose14
. Even with attempts to
better characterize newer scanners (AAPM Task Group 111)13
or adjust CTDI
measurements to account for patient size (AAPM Task Group 204)15
, there is still a need
to develop methods of estimating the dose to patient‘s organs. These estimation methods
must account for the variation in dose due to MDCT scanner differences, the dependence
of dose on patient, and how commonly used dose reduction methods, such as Tube
Current Modulation (TCM) effects organ dose values. The overall purpose of this
dissertation is to address these needs by developing and validating a comprehensive
technique to estimate organ doses to any patient from any scanner that has the capability
of accounting for the effects of various scan protocols, including the use of TCM.

19
Chapter 2 Specific Aims
This research is meant to address the limitations of the current MDCT dosimetry
evaluation paradigm by developing novel methods to obtain accurate and meaningful
patient dose estimates. Despite the inherent advantages of Monte Carlo simulation
methods, it is currently not feasible to assess doses to patients on a routine basis in the
clinic. Therefore, in order to move beyond basic phantom dose measurements, the overall
goal of this work was to derive a more generalizable organ dose estimation method that
could be applied to patients undergoing exams on any 64-slice MDCT scanner. This was
done by first developing a method to accurately model the x-ray source characteristics of
any scanner for use in scanner-specific Monte Carlo simulations. Then, these simulation
models were used to show the feasibility of using the CTDI metric as an index to estimate
dose to a given patient from any scanner. Next, the influence of patient size was
investigated in order to extend the estimation method to predict dose to any patient.
Finally, the estimation method was extended to take into account the effects of tube
current modulation (TCM), a common dose reduction technique. The specific aims of this
work were:
Specific Aim 1: To address the limitations of using manufacturer provided source
information, such as photon energy spectrum and filtration designs, (which is often
proprietary), by presenting a method to derive ―equivalent source models‖ that only
require physical measurements obtained on the scanner of interest. The predictive
accuracy of MDCT Monte Carlo simulations using the equivalent source model were

20
assessed and compared to those using manufacturer provided source models. Specific
Aim 1 is the focus of Chapter 4.
Specific Aim 2: To investigate the feasibility of a scanner-independent technique to
estimate organ doses that utilizes the CTDI as an index of scanner tube output. The use of
universal CTDI-to-organ dose conversion coefficients were evaluated in order to predict
dose for a single patient model. Chapter 5 describes the work used to address Specific
Aim 2.
Specific Aim 3: To account for the effect of patient size on CTDI-to-organ dose
conversion coefficients in order to extend the scanner-independent organ dose estimation
method to any patient. Chapters 6 and 7 cover the studies used to investigate Specific
Aim 3.
Specific Aim 4: To evaluate the effect of tube current modulation (TCM) dose reduction
techniques on organ dose values for a large number of patients. Then, the feasibility of
accounting for TCM effects in the calculation of CTDI-to-organ dose conversion
coefficients was assessed. Specific Aim 4 is addressed in Chapter 8.
Specific Aim 5: To address the limitations of commonly used Monte Carlo validation
techniques and present more advanced benchmarking methods. The preliminary work to
assess Specific Aim 5 is described in Chapter 9.

21
Chapter 3 UCLA Monte Carlo MDCT Dosimetry Package
All MDCT dosimetry simulations discussed in this dissertation were performed
using the UCLA Monte Carlo MDCT dosimetry package31,42
. This package is built on the
MCNPX (MCNP eXtended v2.7.a) Monte Carlo radiation transport code developed at
Los Alamos National Laboratory43,44
. As described in detail below, the MCNPX code
was modified to simulate the delivery of x-ray radiation from specific MDCT scanners.
This package was designed to tally doses in patients or phantoms that are specified using
either simple, geometrical descriptions or detailed, voxelized models.
3.1 Radiation Transport Methods
MCNP is a general-purpose Monte Carlo N-Particle code originally designed for
neutron, photon, electron, or coupled neutron/photon/electron transport. Since then, the
MCNPX code has been developed, which tracks nearly all particles at nearly all
energies.44
This code utilizes a Markov Chain Monte Carlo algorithm which simulates the
passage of one particle at a time through a specified geometry until the particle either
leaves the geometry or falls below a preset energy cutoff. This process is repeated a very
large number of times and each particle is unaffected by the behavior of particles
previously simulated
The MCNPX software package consists of a large number of text files that
contain FORTRAN code segments which, together, facilitate statistical particle transport
calculations. MCNPX problems are defined by user-supplied input files that specify the

22
geometry to transport through and tally various quantities in (including the material and
density descriptions), the types and initial conditions of the particles to transport, and the
desired type of tallies (e.g. fluence or energy deposition). Tallies results are calculated on
a simulated photon basis and are reported along with the relative error of the tally
corresponding to one standard deviation. According to the MCNPX User‘s Manual,
results with errors less than 10% are generally (but not always) reliable.44
3.2 Modifications to Model MDCT Scanners
In order to model the delivery of radiation from MDCT scanners the UCLA CT
dose research group created a subroutine, denoted source.f, to specify the source
characteristics for MCPNX MDCT simulations.31,42
The ultimate goal of source.f is to
establish the energy, initial position and trajectory for each simulated x-ray photon. These
values are all randomly selected from scanner-specific probability distributions. The
methods to obtain scanner-specific energy spectra and filtration descriptions will be
discussed in detail in Chapter 4.
The initial three dimensional position of each photon is selected from continuous
sinusoidal functions describing either single axial, translating axial, or helical paths that
depend on the geometry of the scanner (i.e. source to isocenter distance), starting
longitudinal position, starting gantry angle, nominal collimation width, and pitch for
helical scans. The initial trajectory is specified as a three dimensional unit vector
randomly selected based on the starting position, the scanner‘s fan-angle, and actual
beam width (as opposed to the nominal collimation). The actual beam width was obtained

23
by measuring the longitudinal beam profile for a single axial scan at isocenter using OSL
strips and calculating the full width half max value for each scanner and collimation
combination of interest. The energy of each simulated photon is obtained by randomly
sampling from the probability distribution function describing the photon energy
spectrum of the scanner being simulated.
This method of randomly selecting positions and trajectories from continuous
distributions makes it impossible to explicitly define scanner filtration, which varies as a
function of each photon‘s initial conditions. Instead, attenuation due to filtration
(including the bowtie filter) is modeled using the MCNPX source weight feature. The
source weight is a factor that each particle is multiplied by as it is accepted for
transport.44
For each photon, the source weight is calculated by first using the filtration
description for the particular scanner and bowtie filter setting being simulated to
determine the distance the photon travels through the filter based on the photon‘s
trajectory. Then, the resulting attenuation factor is calculated by assuming exponential
attenuation and using the photon mass attenuation coefficient (μ/ρ) of the filtration
material, published by Hubbell and Seltzer45
and applied as the MCNPX source weight
factor. The source weight factor is also multiplied by a factor to account for the inverse
square intensity drop off of a point source of radiation.
All MCNPX simulations were performed in photon mode with a low-energy
cutoff of 1 keV. In this mode photoelectrons are ignored and all deposited energy is
absorbed at the photon interaction site. This assumption satisfies the condition of charged

24
particle equilibrium (CPE) for which the collision kerma (kinetic energy released in
matter from photoelectrons) is equal to absorbed dose and has shown to be valid for the
diagnostic x-ray energy range.31
Thus, the dose to a volume of interest is given by:
Eq. 3.1
where ψE is the total particle fluence for a given energy in the volume, E is the particle
energy (the product of ψE and E is denoted the energy fluence), and (μen/ρ)E,material is the
energy- and material-dependent mass energy absorption coefficient. For each particle, a
*F4 MCNPX tally type is used to score the energy fluence and the MCNPX dose energy
(DE) and dose function (DF) cards are used to multiply the flux by the (μen/ρ)E,material
values, also published by Hubbell and Seltzer45
.
3.3 Post Simulation Processing
The MCNPX simulations described above return dose values that are normalized
on per simulated photon basis. Furthermore, these simulations do not account for the
specific photon fluence for a given nominal collimation on the MDCT scanner of interest.
As a result, an exposure normalization factor is necessary to both convert MCNPX tally
values from dose/source particle to an absolute dose and to take into account the
dependence of beam collimation on photon fluence.
As defined by Jarry, et al.42
, for a given kVp and nominal collimation (NT),
normalization factors are the ratio of measured CTDIair values (dose from a single

25
rotation measured with a 100 mm ionization chamber positioned in air at isocenter,
normalized by the nominal collimation) to analogous CTDIair simulations:
Eq. 3.2
where the measured CTDIair value is normalized on a per total mAs basis (mGy/total
mAs) and the simulated CTDIair is in units of mGy/simulated photons. The resulting ratio
is in units of simulated photons/total mAs and thus serves as a factor to convert MCNPX
results in mGy/simulated photons to values in mGy/total mAs. Note that the total mAs is
the cumulative mAs value over the entire scan, not the mAs value typically quoted by the
scan protocol which refers to mAs/rotation, so:
Eq. 3.3
Therefore, MCNPX simulation results are converted to absolute dose (in mGy) by the
following expression:
Eq. 3.4
where the last term gives the total number of rotations in the scan.
3.4 Validation of Dose Simulations
The validity of MDCT scanner-specific simulations using the Monte Carlo
package described above depends on the accuracy of a) the radiation transport code
(MCNPX), b) the modeling of the source motion and particle trajectory (source.f), c) the
scanner-specific inputs, such as the geometry specifications, the photon energy spectrum,

26
and the filtration description, and d) the precision of the phantom or patient model. Due
to the difficulties in obtaining an absolute dose measurement in anthropomorphic
phantom discussed in Chapter 1, CTDI100 measurements were used to benchmark the
scanner-specific simulation models described in this dissertation. This was done by first
obtaining exposure measurements for center and peripheral CTDI100 values for both the
32 cm diameter and 16 cm diameter CTDI phantoms for 64-slice MDCT scanners from
the four major scanner manufacturers, including: The LightSpeed VCT (General Electric
Medical Systems, Waukesha, WI), SOMATOM Sensation 64 (Siemens Medical
Solutions, Inc, Forcheim, Germany), Philips Brilliance CT 64 (Philips Medical Systems,
Cleveland, Ohio), and Toshiba Aquilion 64 (Toshiba Medical Systems, Inc., Otawara-shi,
Japan). These measurements were obtained on a per mAs basis for all available kVp
values for a number of nominal collimation settings on each scanner. Next, analogous
CTDI100 simulations were performed using the source models for each scanner.
The source models and accuracy of the corresponding CTDI100 benchmark
simulations will be described in detail in Chapter 4. Furthermore, since the CTDI is dose
to a simple, homogenous phantom it is limited in evaluating the accuracy of detailed
patient simulations, so more advanced validation methods will be discussed in Chapter 9.

27
Chapter 4 A Method to Generate Equivalent MDCT Source Models Based on
Measurements†
4.1 Introduction
An accurate MDCT Monte Carlo simulation typically requires a detailed
description of the scanner under investigation, including specifications of the photon
energy spectrum, the bowtie and inherent filtration design, and the geometry of the
scanner (e.g. focal spot to isocenter distance, fan angle, z-axis collimation, cone angle
settings, etc.). It is usually possible to ascertain the necessary geometry from
documentation of scanner specifications. However, scanner-specific source descriptions
that include filtration designs and spectra are typically proprietary, so vendor cooperation
through non-disclosure agreements (or equivalent) has been required to obtain this
information. While in some cases published generalized tungsten anode energy spectra,
either from empirically measured or theoretical models, have been used in Monte Carlo
simulations46
, there is no such published data on the design of bowtie and inherent
filtration, which may vary considerably from scanner to scanner. As a consequence,
†
This chapter is based on the following publication:
A. C. Turner, D. Zhang, H. J. Kim, J. J. DeMarco, C. H. Cagnon, E. Angel, D. D. Cody, D. M.
Stevens, A. N. Primak, C. H. McCollough, and M. F. McNitt-Gray, ―A method to generate
equivalent energy spectra and filtration models based on measurement for multidetector CT
Monte Carlo dosimetry simulations,‖ Med. Phys. 36(6), 2154–2164 (2009).

28
MDCT Monte Carlo dosimetry simulations have been performed by a limited number of
researchers who normally can only investigate a small subset of existing scanners for
which they have obtained confidential information to build their source models.
In order to overcome such restrictions the purpose of this work is to introduce a
method to construct source models that only requires physical measurements and
calculations. The goal of this method is to generate an ―equivalent‖ source model that
consists of two parts. The first part is an equivalent energy spectrum, defined as ―an
idealized energy spectrum which results in identical attenuation properties as the actual
spectrum of a 47
‖. The second part is an equivalent filter description, defined as an
idealized filter that attenuates the equivalent spectrum in the same manner that the actual
filter attenuates the actual spectrum (including bowtie filtration and its variation across
the fan angle). Such an approach obviates the need for obtaining proprietary information
and allows the generation of source models to characterize any given scanner. Since this
method is designed to require only measured data taken from the scanner of interest it
should result in more accurate scanner-specific Monte Carlo dosimetry simulations
compared to those that use generic source models.
In this study, first the scanner measurements and calculations necessary to
generate equivalent source models are presented. Then, the predictive accuracy of
equivalent source model MDCT Monte Carlo simulations will be assessed by comparing
the results of multiple CT dose index (CTDI) simulations performed using equivalent
source models with a previously presented Monte Carlo software package31,42
to

29
physically measured CTDI values. Finally, equivalent source model simulations will be
evaluated relative to conventional manufacturer-based source model simulations, first by
comparing the accuracy of CTDI simulations using each type of source model and then
through an analysis of variance to determine if these source models produce statistically
different simulation results.
4.2 Methods
4.2.A. CT Scanner Models
4.2.A.1. The CT Scanners
To investigate the robustness of the proposed method, 64-slice CT scanners from
four major CT scanner manufacturers were included in this study: the LightSpeed VCT
(General Electric Medical Systems, Waukesha, WI), SOMATOM Sensation 64 (Siemens
Medical Solutions, Inc, Forcheim, Germany), Philips Brilliance CT 64 (Philips Medical
Systems, Cleveland, Ohio), and Toshiba Aquilion 64 (Toshiba Medical Systems, Inc.,
Otawara-shi, Japan). Each of these is a third generation, multidetector row CT scanner
that supports multiple nominal beam collimation settings as well as multiple beam
energies. Each scanner is equipped with x-ray beam filtration that includes from one to
three bowtie filter combinations. For this study each different scanner and bowtie filter
combination was assessed separately (the GE LightSpeed VCT has three bowtie filter
settings, the Toshiba Aquilion 64 has two, while the Siemens Sensation 64 and Philips
Brilliance 64 each have one, resulting in seven unique scanner/bowtie filter

30
combinations). Each of the scanner/bowtie filter combinations was randomly assigned a
reference letter, either A, B, C, D, E, F, or G and will be referred to by their assigned
letter from this point on.
4.2.A.2. Source Models based on Manufacturer-Provided Information
Data describing the x-ray source for each scanner described in 4.2.A.1 was
obtained from the manufacturers under a non-disclosure agreement. Each manufacturer
provided a description of the x-ray energy spectra representing the relative number of
photons at each energy level for each available kVp setting. Additionally, they provided
specifications of scanner filtration by specifying the dimensions and materials of all
available bowtie filters as well as the design of any other inherent filtration. The scanner
geometry necessary for the Monte Carlo simulations, namely the focal spot to isocenter
distance and fan angle, were also obtained directly from the manufacturers; however, this
information is usually available in user manuals or specification sheets included in CT
scanner documentation.
4.2.B. Measurements to Generate Equivalent Source Models
4.2.B.1 Overview of Physical Measurements Used to Generate Equivalent Source
Models
The scanner measurements required of this method are generally not part of
routine medical physics measurements for CT, but can be performed reasonably quickly
and efficiently with commonly used equipment. It should be noted that some scanners

31
must be put into service mode because these measurements are performed with a non-
rotating (stationary) gantry. For each scannerbowtie filter combination, two types of
measurements were obtained: (a) half and quarter value layers (HVL and QVL, note that
these will be referred to as HVL measurements) and (b) bowtie filter attenuation profiles.
Each requires a set of exposure measurements which were performed with a standard 100
mm pencil ionization chamber (ion chamber) and calibrated electrometer.
4.2.B.2. Half Value Layer Measurements
The method used to measure MDCT HVL values is similar to standard HVL
measurements used for conventional radiograph machines. The gantry was parked so that
the x-ray tube remained stationary at the 6 o‘clock position. The ion chamber was fixed
along the central ray (directly above the stationary x-ray tube), ensuring the table was not
in the x-ray beam path, at a distance above the source sufficient to establish good
measurement geometry (for all measurements the ion chamber was positioned at or above
the scanner isocenter). An initial exposure value was taken using a particular kVp, mAs,
and collimation setting. Additional exposure measurements were obtained using the same
settings, adding thin slabs (0.5 mm – 2.0 mm) of type 1100 alloy aluminum in the beam
path until the resulting exposure was less than half the initial value to obtain the HVL and
less than a quarter of the initial value to obtain the QVL. The experimental set up is
illustrated in Figure 4.1. For scanner/bowtie filter combinations A-G, measurements were
performed to determine the HVL and QVL for all available beam energies.

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