3. Abstract
Development of a multiknifeedge slit collimator for prompt gamma ray imaging during
proton beam cancer therapy
by
John Francis Ready III
Doctor of Philosophy in Nuclear Engineering
University of California, Berkeley
Professor Kai Vetter, Chair
Proton beam usage to treat cancer has recently experienced rapid growth, as it
offers the ability to target dose delivery in a patient more precisely than traditional xray
treatment methods. Protons stop within the patient, delivering the maximum dose at the
end of their track—a phenomenon described as the Bragg peak. However, because a large
dose is delivered to a small volume, proton therapy is very sensitive to errors in patient
setup and treatment planning calculations. Additionally, because all primary beam particles
stop in the patient, there is no direct information available to verify dose delivery. These
factors contribute to the range uncertainty in proton therapy, which ultimately hinders its
clinical usefulness. A reliable method of proton range verification would allow the clinician
to fully utilize the precise dose delivery of the Bragg peak.
Several methods to verify proton range detect secondary emissions, especially
prompt gamma ray (PG) emissions. However, detection of PGs is challenging due to their
high energy (2–10 MeV) and low attenuation coefficients, which limit PG interactions in
materials. Therefore, detection and collimation methods must be specifically designed for
prompt gamma ray imaging (PGI) applications. In addition, production of PGs relies on
delivering a dose of radiation to the patient. Ideally, verification of the Bragg peak location
exposes patients to a minimal dose, thus limiting the PG counts available to the imaging
system.
An additional challenge for PGI is the lack of accurate simulation models, which limit
the study of PG production characteristics and the relationship between PG distribution
and dose delivery. Specific limitations include incorrect modeling of the reaction cross
sections, gamma emission yields, and angular distribution of emission for specific photon
energies. While simulations can still be valuable assets in designing a system to detect and
image PGs, until new models are developed and incorporated into Monte Carlo simulation
packages, simulations cannot be used to study the production and location of PG emissions
during proton therapy.
1
4.
This work presents a novel system to image PGs emitted during proton therapy to
verify proton beam range. The imaging system consists of a multislit collimator paired
with a positionsensitive LSO scintillation detector. This innovative design is the first
collimated imaging system to implement twodimensional (2D) imaging for PG proton
beam range verification, while also providing a larger field of view than compared to
singleslit collimator systems. Other, uncollimated imaging systems have been explored for
PGI applications, such as Compton cameras. However, Compton camera designs are
severely limited by counting rate capabilities. A recent Compton camera study reported
count rate capability of about 5 kHz. However, at a typical clinical beam current of 1.0 nA,
the estimated PG emission rate would be 6 x 108
per second. After accounting for distance
to the detector and interaction efficiencies, the detection system will still be overwhelmed
with counts in the MHz range, causing false coincidences and hindering the operation of the
imaging system.
Initial measurements using 50 MeV protons demonstrated the ability of our system
to reconstruct 2D PG distributions at clinical beam currents. A Bragg peak localization
precision of 1 mm (2σ) was achieved with delivery of (1.7 ± 0.8) x 108
protons into a
poly(methyl methacrylate) (PMMA) target, suggesting the ability of the system to detect
relative shifts in proton range while delivering fewer protons than used in a typical
treatment fraction. This is key, as the ideal system allows the clinician to verify proton
range when delivering only a small portion of the prescribed dose, preventing the
mistreatment of the patient. Additionally, the absolute position of the Bragg peak was
identified to within 1.6 mm (2σ) with 5.6 x 1010
protons delivered.
These promising results warrant further investigation and system optimization for
clinical implementation. While further measurements at clinical beam energy levels will be
required to verify system performance, these preliminary results provide evidence that 2D
image reconstruction, with 1–2 mm accuracy, is possible with this design. Implementing
such a system in the clinical setting would greatly improve proton therapy cancer
treatment outcomes.
2
5. Dedication
To my wife Miriam—for your unending support, patience, and love. I wouldn’t have
gotten through this doctorate if not for you. I strive each day to make you as proud of me as
I am of you.
To my son Logan and daughter Lexi—you both came into the world during my
graduate school career and have completely changed my perspective on life. Your smiles
and love have provided me with more motivation than you will ever know.
i
6.
Table of Contents
List of Figures iv
List of Tables vii
Acknowledgements viii
Chapter 1—Introduction 1
Protons for cancer treatment 1
Physics of proton therapy 2
Range uncertainty 4
Motivation & challenges 5
Chapter 2—Techniques and Challenges of Range Verification 7
Positron emission tomography 7
PG emission 8
Detection and imaging of PGs 10
Detector materials 10
Detection techniques 11
Compton cameras 11
Electrontrack Compton cameras 11
Gammaelectron vortex imaging 12
PG timing 12
Collimated cameras 12
Conclusion 13
Chapter 3—A Monte Carlo Study of Prompt Gamma Production 14
Background 14
PG production cross sections 15
Methods 15
Results 16
Angular distribution of PGs 18
Methods 20
Results 21
Conclusion 23
Chapter 4—A Novel MultiSlit Collimated Imaging System 24
Introduction 24
Collimator design 25
Detector configuration 27
Conclusion 30
ii
7. Chapter 5—Data acquisition, data processing, and image reconstruction 31
Acquisition hardware 31
Detectors 32
Highvoltage power supply 32
AnalogtoDigital Converter 33
Acquisition software and data processing 35
Acquisition 35
Data processing 37
Image reconstruction 40
Chapter 6—Simulation Study of Collimator Performance 43
Introduction 43
Methods and materials 43
Results and discussion 46
Conclusion 48
Chapter 7—Experimental Characterization of System Performance 50
Introduction 50
Methods and materials 50
Results and discussion 52
Image reconstruction 52
Range measurement 54
Conclusion 59
Chapter 8—Conclusion 61
Summary 61
Key challenges remaining 62
Directly applicable to this PGI system 62
Detector design 62
Optimization of the image reconstruction algorithm 63
Collimator design 63
PGI field at large 64
Monte Carlo simulations of PG production 64
Effect of neutrons on imaging system 64
Development of correlation between PG distribution and
Bragg peak location
64
Inhomogeneous target materials 64
Clinical implementation of proton range verification 65
Final thoughts 65
References 67
iii
8.
List of Figures
Figure 1 Depthdose distributions for a SpreadOut Bragg Peak (SOBP), its
constituent pristine Bragg peaks, and a 10 MV photon beam. 3
Figure 2 Comparison of treatment planning dose distributions for proton therapy
treatment of prostate cancer with one single anterior field and two
parallelopposed lateral fields. 5
Figure 3 Simulated profiles for dose, PG, and PET integrated over the entire beam
for an abdomen irradiated with pencil beams. 8
Figure 4 Production cross sections for PGs resulting from inelastic collisions of
protons with 16
O and 12
C. 10
Figure 5 Comparison of simulation results with experimental data for 1.63 MeV
gamma production during proton irradiation of thin nitrogen target. 16
Figure 6 Comparison of simulation results with experimental data for 2.31 MeV
gamma production during proton irradiation of thin nitrogen target. 17
Figure 7 Comparison of simulation results with experimental data for 4.44 MeV
gamma production during proton irradiation of thin carbon target. 17
Figure 8 Examples of angular distributions of gamma rays. 19
Figure 9 Angular distributions for five separate gamma ray transitions resulting
from bombardment of Mylar, Mg, Si, and Fe targets with 33 MeV protons. 20
Figure 10 Angular distribution of 4.4 MeV PGs (Geant4). 22
Figure 11 Comparison of theoretical isotropic PG distribution and expected gamma
distribution as detected based on angular distribution. 22
Figure 12 CAD model of imaging system showing 4 x 4 grid of LSO detector modules,
tungsten collimator with individually cut pieces held together with 3D
printed plastic, and a proton beam incident on a plastic target. 24
Figure 13 Fan beam collimator design modeled in TOPAS. 25
Figure 14 Arrangement and dimensions of tungsten collimator pieces. 26
Figure 15 CAD model to demonstrate the position and layout of LSO detector
modules. 28
Figure 16 Photographs of bottom two rows of detector modules. 28
Figure 17 Photograph of a detector module and connections. 29
Figure 18 Gain adjustment on PMTs is performed by making slight adjustments in the
respective gain adjustment potentiometer. 29
Figure 19 Photo of acquisition system hardware. 31
Figure 20 Electronics in VME crate. 32
Figure 21 Screenshot of HV power supply control interface. 33
Figure 22 Image of the four SIS33316 Struck ADCs. 34
iv
9.
Figure 23 The 4 x 4 detector array forms the LSO GAmma detection iNstrument
(LOGAN) that is powered by the HV power supply to transfer the light
signal produced in the scintillation crystal to an electrical signal generated
in the PMTs which is then sent to the ADCs. 34
Figure 24 Back view of the LEMOtoEthernet for Xray Imaging (LEXI) apparatus. 35
Figure 25 Firmware schematic of 4 Channel Sum Trigger sequence. 36
Figure 26 Screenshot of raw signal data from a detector module. 37
Figure 27 Image representing the PMT configuration in relation to the segmented
scintillator crystal. 38
Figure 28 Example map of detector interactions as calculated using Equations 5 and
6. 38
Figure 29 Histogram of gain correction factors used to ensure a more uniform
response across the detector array pixels. 39
Figure 30 Map of gain correction factors used to account for variations in counting
rates of individual detector pixels. 39
Figure 31 Example of ray tracing result for a point source projection through the
collimator onto the detector plane. 41
Figure 32 Sensitivity map generated by the ray tracing algorithm. 42
Figure 33 Simulation of collimator and flat detector panel in TOPAS. 44
Figure 34 Simulated point sources shifted in 1 cm steps along the xdirection. 45
Figure 35 1D reconstructed image of point sources at varying locations which
demonstrates, via FWHM,the spatial resolution across one dimension. 46
Figure 36 2D reconstruction of a simulated point source at (x, y) position (15, 5). 47
Figure 37 2D reconstruction of a simulated point source at (x ,y) position (5, 10). 47
Figure 38 Input and reconstructed response of a 4.4 MeV line source. 48
Figure 39 Proton range in tissueequivalent plastic (PMMA) for proton energy 0–170
MeV and 050 MeV. 51
Figure 40 Photo of experimental setup at 88Inch Cyclotron. 51
Figure 41 Photo of PMMA target in place for Bragg peak measurements with 50 MeV
proton beam. 52
Figure 42 2D image reconstruction of 50 MeV proton beam in PMMA target. 53
Figure 43 1D image of PG distribution plotted with the simulated Bragg curve with
dose normalized to the maximum integral depth dose. 53
Figure 44 Range retrieval precision (2 ) versus the number of delivered protons. 54
Figure 45 Estimated target position and deviation from trend line. 55
Figure 46 Estimated Bragg peak location and residuals. 56
Figure 47 2D image reconstruction of 50 MeV proton beam in the thick PMMA
target, separated by photon energy. 57
v
10.
Figure 48 1D images of PG distribution, sorted by photon energy, plotted with the
simulated Bragg curve with dose normalized to the maximum integral
depth dose. 57
Figure 49 2D image reconstruction of 50 MeV proton beam in the thick PMMA
target, separated by photon energy with target location shifted 3 mm to the
right relative to Figure 47. 58
Figure 50 1D images of PG distribution, sorted by photon energy, plotted with the
simulated Bragg curve with dose normalized to the maximum integral
depth dose with target location shifted 3 mm to the right relative to Figure
48. 58
Figure 51 Positional uniformity of detection system. 59
Figure 52 Energyintegrated and discrete PG emissions along the path of proton
pencilbeams in water. 60
Figure 53 Ageadjusted invasive cancer incidence rates in 2012 for the ten primary
sites with the highest rates in men. 66
vi
12.
Acknowledgements
"... to know even one life has breathed easier because you have lived—this is to
have succeeded."—Bessie Anderson Stanley, as abridged by Albert Edward
Wiggam
My time at Berkeley has been filled with opportunities for personal and professional
growth. I have been humbled and honored by the chance to serve my communities: in
particular, veterans and graduate students through involvement in the Graduate Assembly.
The time and effort I have committed to fostering a vibrant community at Cal has been
returned to me many times over in emotional and social support and encouragement, and
it introduced me to mentors such as Ron Williams, Dean Joseph Greenwell, and Vice
Chancellor Harry Le Grande. While a PhD is largely viewed as an accomplishment of the
individual, as with any major personal endeavor, my PhD and dissertation would not be
possible without the support of a loving community. I therefore attribute the successful
closure of this chapter of my life to the vast array of humans that I have had the great
pleasure of knowing and working with over the past six years.
I would like to express my deepest gratitude to my supervisor Professor Kai Vetter
for his unwavering support, collegiality, and mentorship throughout this project.
I wish to thank my committee members who were more than generous with their
expertise and precious time.
I would like to extend my thanks to those who offered invaluable research guidance
and support over the years: Victor Negut; Rebecca Pak; Ryan Pavlovsky; Sam Huh; Tim
Aucott; Andy Haefner; Ross Barnowski; Don Gunter; Lucian Mihailescu; Justin Ellin; Joseph
Perl; Bill Moses; fellow graduate students, postdocs, and staff scientists of the Applied
Nuclear Physics Program; and countless others who have had a positive impact on my
work.
Thanks to my dad, John; mom, Micki; motherinlaw, Lucy; brothers, Joey and
Jimmy; and all my family members, friends, fellow student veterans, Graduate Assembly
colleagues, and anyone else that has knowingly or unknowingly helped me along my path.
Finally, I offer my sincere appreciation to the staff and facilities at Lawrence
Berkeley National Laboratory; the faculty, staff, and students who supported my
educational experience at UC Berkeley; and my funding source—the Nuclear Science and
Security Consortium—for providing me with the opportunity and freedom to contribute to
the scientific knowledge of the important field of nuclear science.
viii
13. This material is based upon work supported by the Department of Energy National
Nuclear Security Administration under Award Number: DENA0000979 through the
Nuclear Science and Security Consortium.
This report was prepared as an account of work sponsored by an agency of the
United States Government. Neither the United States Government nor any agency thereof,
nor any of their employees, makes any warranty, express or limited, or assumes any legal
liability or responsibility for the accuracy, completeness, or usefulness of any information,
apparatus, product, or process disclosed, or represents that its use would not infringe
privately owned rights. Reference herein to any specific commercial product, process, or
service by trade name, trademark, manufacturer, or otherwise does not necessarily
constitute or imply its endorsement, recommendation, or favoring by the United States
Government or any agency thereof. The views and opinions of authors expressed herein do
not necessarily state or reflect those of the United States Government or any agency
thereof.
ix
14.
Chapter 1—Introduction
Protons for cancer treatment
The use of radiation for the treatment of cancer began very shortly after the
discovery of Xrays in 1895 [1]. With advances in technology and an increasing
understanding of radiation, radiotherapy has now become a standard treatment option for
a wide range of malignancies [2]. Today, approximately 50% of all patients with localized
malignant tumors are treated with radiation [3]. In theory, any tumor can be killed with a
large enough dose. However, the tolerance of healthy tissue surrounding the tumor volume
limits the radiation dose permitted in practice. Technical advances in radiation therapy
have been aimed mainly at reducing dose to healthy tissue while maintaining or increasing
the dose to the tumor volume. In addition to technical innovations in the “traditional”
photon and electron radiotherapy methods, physicists have searched for alternative
particles that offer advantages in their dose deposition and biological characteristics [4].
First described in 1946 by Robert Wilson [5], the clinical potential of proton beams
lies in the basic physics of heavy charged particle interactions in matter. Wilson recognized
that the depthdose profile of protons in a patient holds dosimetric advantages over
traditional photon (Xray) therapy, which sparked his interest in developing protons for
use in tumor treatment.
The first clinical use of protons occurred at Lawrence Berkeley Laboratory in 1957
[6]. The work at Berkeley confirmed the predictions of Wilson and led to the use of heavy
charged particles in treating human diseases associated with the malfunctioning of the
pituitary gland [7,8]. The following decades saw gradual growth in both the types of
cancers treated with protons and the number of medical centers offering proton therapy
[9]. More recently, there has been a rapid proliferation of proton therapy centers, growing
from approximately 30 to 60 centers worldwide from 2010 to 2015 (with another 30
under construction in 2016) [10]. This rapid expansion of proton therapy has been met
with questions and controversy, as clinicians try to balance efficacy and rising costs of
treatment [11,12].
The properties of proton therapy introduce unique challenges to the field of medical
physics [13]. As the peak dose is delivered over a relatively small volume, proton therapy is
very sensitive to errors in radiation delivery. Mistakes in patient setup or uncertainty in the
treatment planning process can have drastic consequences in the final dose location.
Additionally, because all primary beam particles stop in the patient, there is no current
method available to verify dose is delivered as planned. A solution to address these main
challenges limiting the full optimization of proton therapy is the subject of the following
sections and chapters of this work.
1
15.
Physics of proton therapy
Whereas a photon beam has an exponentially decreasing dose distribution after a
short buildup, the distribution of the proton beam approaches a maximum near the end of
its range. A heavy charged particle (e.g., proton) traveling through matter loses energy
primarily through the ionization and excitation of atoms. Except at low velocities, protons
lose a negligible amount of energy in nuclear collisions [14], and a proton can transfer only
a small fraction of its energy in a single electronic collision. Thus, a proton travels an almost
straight path through matter, losing small amounts of energy almost continuously through
collisions with atomic electrons, leaving ionized and excited atoms in its wake.
The average linear rate of energy loss for a proton in a medium is called the
stopping power—also termed the linear energy transfer (LET). Quantum mechanically, the
stopping power is the mean, or expectation, value of the linear rate of energy loss [14]. This
energy distribution can be described by the BetheBloch equation [15,16]. Mathematically,
the rate of energy loss per unit length for a proton with velocity, v, can be formulated as
, − dx
dE
= 4π
m ce 2 ∙ n
β2 ∙ ( e2
4πε0
)
2
∙ ln[ (2m c βe
2 2
I∙ 1−β( 2
))− β2
] (1)
where E = proton energy
x = proton depth in material
e = electron charge
me = electron rest mass
c = speed of light in vacuum
n = electron density
β = c
υ
I = mean excitation potential
ε0 = vacuum permittivity.
Driven by the term in the denominator, the stopping power increases in β2
proportion to 1/E as the proton slows down and approaches zero velocity [17]. In other
words, as a proton slows down in tissue, it interacts with more electrons. These
interactions reach a maximum at the end of beam range, where the proton slows down,
collects electrons, and delivers the final dose over a very small area. The profile of the
energy loss as a function of distance is termed the Bragg peak.
As shown in Figure 1, the Bragg peak offers two advantages over traditional photon
therapy: 1) the entry (proximal) dose delivered to the patient remains relatively low up
until the Bragg peak region, and 2) the peak dose delivery is found in a small, precise area
near the end of the proton range. The absence of an exit dose offers the opportunity for
highly conformal dose distributions, while simultaneously limiting the irradiation of
normal tissue.
2
16.
Figure 1. Depthdose distributions for a SpreadOut Bragg Peak (SOBP, dashed blue line), its
constituent pristine Bragg peaks (thin blue lines), and a 10 MV photon beam (red). In a typical
treatment plan for proton therapy, the SOBP is the therapeutic radiation distribution. The SOBP is the
sum of several individual Bragg peaks at staggered depths. The pink area represents additional doses
of photon therapy—which can damage normal tissues and cause secondary cancers, especially of the
skin [18].
The depth of the Bragg peak in a patient is directly related to the initial energy,
Einitial, of the charged particle. We can formulate the range, R, of a proton in a homogenous
material by assuming it enters the material with Einitial and summing the energy loss in very
thin slabs until the energy reaches some very low final value, Efinal, as
.R (E )initial = ∫
Efinal
Einitial
(dx
dE
)
−1
(2)
This energy dependence allows the clinician to place the Bragg peak, and thus the dose,
anywhere in the patient. For irradiation of a tumor, the proton beam energy and intensity
are varied in order to achieve the desired dose over the tumor volume. A single clinical
proton field, in contrast to a single photon field, can achieve dose conformation to the
target volume [19]. In general, proton therapy reduces irradiation to normal tissue, while
permitting dose escalation to levels not achievable with standard techniques, improving
clinical outcomes [20].
By superimposing several pristine proton beams with different proton energies
(and hence different proton beam ranges), a SpreadOut Bragg Peak (SOBP) can be shaped
to precisely match tumor tissue. The total energy deposited in a patient (integral dose) for
3
17.
a given target dose is always lower for proton treatments when compared to photon
treatment techniques [21]. The high level of control afforded by proton beams is preferred
for treating tumors located close to critical organs, such as the spinal cord, eye, and brain,
where even a small dose can cause very serious consequences.
Range uncertainty
One major factor limiting the clinical effectiveness of proton therapy is range
uncertainty. With current technology, there is no method to verify that the proton dose was
delivered as planned. Thus, clinicians are completely dependent on treatment planning
calculations for dose delivery. In principle, one should be able to use a proton beam pointed
directly at a critical structure, with the beam energy tuned to stop just short of the critical
organ while delivering maximum dose to the tumor volume. However, range uncertainties
require the addition of substantial safety margins to overirradiate the tumor.
Paganetti [21] gives a thorough review of the types and causes of proton range
uncertainties. In summary, uncertainties result from four main categories: 1) patient
motion; 2) variations in setup and anatomy; 3) approximations used in dose calculations;
and 4) biological effects. With no way to verify dose delivery, clinicians add safety margins
to ensure total irradiation of the target volume. For example, at the Massachusetts General
Hospital (MGH), treatment plans add 3.5% of the range plus an additional 1 mm [21].
Although this guarantees the coverage of the distal aspect of the target volume, it also risks
overdosing the normal tissue behind the target volume. The potential for overdosing
changes the irradiation strategies used for certain cancers, potentially limiting the
usefulness of the treatment.
The most prevalent example of the limitations of current methods is the treatment
of prostate cancer. Naturally, the most effective approach irradiates the tumor from the
anterior (Figure 2a) so that the sharp distal falloff of the proton beam can be used to treat
the target volume while sparing the rectum. This would require, however, a precise control
of the beam range in the patient with millimeter accuracy, which is not currently possible.
As a result, anterior fields have never been used, despite the fact that such fields can utilize
sharp distal penumbra (~4 mm for 50–95% falloff) [22]. Instead, only lateral fields are
used (Figure 2b), relying solely on the much broader lateral beam penumbra (>10 mm for
50–95% falloff) and delivering a larger integral dose to the patient [22].
As a result of these additional margins, tissues distal to the target volume receive a
substantial dose, and the dosimetric benefits of proton therapy are lost. Subsequently,
clinical outcomes of some common treatment sites, such as the prostate, are essentially
equivalent between proton and Xray modalities [23]. With no clear clinical benefit in cases
such as the prostate, questions arise as to whether proton therapy is worth the cost
(approximately double that of treatment with photons).
4
18.
Figure 2. Comparison of treatment planning dose distributions for proton therapy treatment of
prostate cancer with (a) one single anterior field and (b) two parallelopposed lateral fields. Structures
of bladder, bladder wall, prostate, anterior rectal wall, rectum, and femoral heads are shown by cyan
lines. Adapted from [24].
As a note of additional concern, the sharp distal dose falloff of the Bragg curve also
makes the dose distribution extremely sensitive to uncertainties in treatment planning and
patient setup. If planning calculations are off by 1 cm, the location of the distal falloff will
change by 1 cm, causing either an undershoot, which is to underirradiate the distal portion
of the target volume, or an overshoot, where the full dose is delivered to normal tissue
behind the target volume.
Motivation and challenges
Debates amongst medical physicists regarding the cost and efficacy of proton
therapy start with consideration for the limitations of currently available technologies
[25,26]. As described above, the underlying physics of proton therapy offer clear potential
benefits in the radiotherapy treatment of cancer. Because of range uncertainty and the lack
of method to verify range at delivery, the benefits of proton therapy suggested by the
physical principles of charged particle interactions cannot be realized. When a reliable
method of range verification is incorporated into the cancer treatment process, the full
potential of proton therapy will be available for utilization by clinicians.
The work described herein presents a system for imaging prompt gamma rays
emitted during proton therapy as a method of range verification. This innovative design is
the first collimated imaging system to implement twodimensional (2D) imaging for
prompt gamma proton beam range verification. This work is timely because as proton
treatment facilities continue to proliferate, accurate and effective range verification is
essential to ensuring the efficacy and optimization of the treatment method.
5
19.
Due to treatment modalities currently available clinically, this work is focused
mainly on proton therapy. However, there is similarly much interest in other heavy ion
beams for cancer treatment, such as carbon [27–32]. With physical and biological
differences that offer potential benefits over protons, carbon ions will likely find their way
into the clinical mainstream in the near future [33,34]. However, the same challenges
regarding range verification exist with heavier ions. The method of range verification
proposed in this work could be directly applied to the case of carbon ions or any other
heavy ion treatment. As with proton therapy, a reliable range verification technique would
have a positive impact on the clinical efficacy of any heavy ion therapy. The importance and
potential impact of this research cannot be overstated.
In this work, Chapter 2 discusses the technical challenges associated with proton
range verification, such as high photon energy, high background environment, and a lack of
imaging devices suitable for proton therapy application. Other methods of range
verification under investigation are also discussed. Chapter 3 describes the benefits and
challenges in using simulation tools to aid with research efforts related to prompt gamma
imaging. Chapters 4–7 characterize the design and performance of an imaging system
developed to address these challenges, while Chapter 8 summarizes the implications of this
imaging system. The ultimate goal of this work is to determine proton beam range to an
accuracy of a few millimeters, which has the potential to make a significant impact on
proton therapy and the entire radiation oncology field.
6
20.
Chapter 2—Techniques and Challenges of Range
Verification
A reliable method of range verification would improve the clinical capability of
proton therapy by allowing for more precise targeting during treatments and increase our
understanding of range uncertainties. Because all primary particles are stopped inpatient,
research has focused on using secondary emissions to verify proton range. Of the several
techniques currently being studied for in vivo range monitoring during proton therapy, the
two most developed methods involve the imaging of secondary coincident positron
annihilation photons and characteristic prompt gamma (PG) rays emitted as a result of 1
proton beam irradiation of the patient [35].
Positron emission tomography
Positron emission tomography (PET) imaging has been widely studied for use in
proton range verification. This clinically tested technique involves moving the patient to a
PET scanner immediately after proton irradiation [36]. The method has shown some ability
to accurately measure the in vivo proton beam range. However, its implementation has
been limited due to scanner resolution, positron energy, and biological washout [37].
Additionally, because proton beams lose energy mainly via electromagnetic interactions,
the activation image from positron emitters generated by nuclear reactions is not directly
correlated to the dose distribution. The established method uses a Monte Carlo calculated
distribution of the positron emitters and compares this predicted image with a measured
image. The accuracy of the Monte Carlo calculation depends on the underlying cross section
data [21].
Efforts are underway to develop inbeam PET which would add an imaging system
to the proton gantry [38–40]. This technique could eliminate problems associated with
biological washout; however, there are additional problems created with the geometry of a
conventional PET scanner, which, due to its large size limits its installation in the proton
treatment room, as well as a poor signaltonoise ratio experienced with attempts of PET
verification during beam delivery [41]. The positron emitting nuclides most produced
during proton therapy (15
O, 11
C, 30
P, and 38
K) have radioactive decay halflives between
2–20 mintues, providing time for biological washout effects over the few minutes of
required image acquisition time. Alternatively, the positron endpoint energy of some
shortlived positron emitters of interest for PET imaging during the proton treatment,
produced in high concentrations and of clinical interest, is too high to offer any benefit in
1
Recent literature uses the terms “prompt gamma ray” and “prompt gamma” interchangeably. For
consistency and clarity, the acronym PG is used throughout the text in place of both terms. In either case, PG
refers to the nearinstantaneous photon emitted following the deexcitation of a nucleus. PGI refers to the
imaging of prompt gamma rays.
7
21.
proton range verification applications (such as the 16.3 MeV positron endpoint for 12
N,
with a range of ~2 cm) [42].
PG emission
The drawbacks of PET have fueled interest in the development of prompt gamma
imaging (PGI) during proton therapy. PGs are emitted instantaneously (on the order of a
few tens of femtoseconds) following an inelastic collision of a proton with a target nucleus.
Like PET, PGs are produced by nuclear reactions. However, in PGI the measured photons
are emitted directly from the nucleus as opposed to PET photons emitted some distance
from their origin following an electronpositron annihilation. Thus, the effects of biological
washout and decay time seen with the PET method are nonexistent in PGI. In a direct
comparison with PET, PG was found to have an approximately 10fold larger production
rate and to have a distribution physically much closer to the Bragg peak [43]. Thus, the
spatial distribution (Figure 3) of the induced activity correlates better with absorbed dose
for PG as compared to PET [44]. The PG production is highest at low proton energies, as
shown in Figure 4. This results in more PG emissions in the vicinity of the Bragg peak.
Figure 3. Simulated profiles for dose (black), PG (blue), and PET (red) integrated over the entire beam
for an abdomen irradiated with pencil beams. All profiles are normalized to unity for easier
comparison. The xaxis represents the position along the beam direction relative to the isocenter.
Adapted from [43].
Unlike PET, which uses a wellestablished imaging modality for photon energy of
511 keV, PGs pose an imaging challenge due to their high energy (2–10 MeV). The
advantage of focusing detection efforts on highenergy gamma rays is the ability to filter
out lower energy photons that are also produced by the proton beam, such as the 511 keV
8
22.
photons, which are not produced as close to the Bragg peak as PGs. Classical gamma
cameras used in nuclear medicine are not adapted for detection of highenergy gammas in
the presence of an important neutron background, so dedicated cameras are needed.
Imaging highenergy photons requires specialized materials and methods.
Table 1. Reaction channels that produce the most prominent gamma energies [45].
Target Reaction Eγ
(keV) Halflife
C 12
C(p,p’)12
C 4443 45 fs
13
C(p,d)12
C 4443 45 fs
12
C(p,n)12
N 4443 11 ms (β+
➔ 12
C*
)
13
C(p,2p)12
N 4443 11 ms (β+
➔ 12
C*
)
N 14
N(p,p’)14
N 1635 4.8 fs
14
N(p,n)14
O 1635 71 s (β+
➔14
N*
)
14
N(p,p’)14
N 2313 68 fs
14
N(p,n)14
O 2313 71 s (β+
➔ 14
N*
)
O 16
O(p,pα)12
C 4443 45 fs
16
O(p,nα)12
N 4443 11 ms (β+
➔ 12
C*
)
16
O(p,d)15
O 5241 122 s (β+
➔ 15
N*
)
16
O(p,2p)15
N 5270 17 fs
16
O(p,p’)16
O 6129 18 ps
A sampling of characteristic gamma rays produced during proton therapy is listed in
Table 1. It is important to note the many different protoninduced reaction channels
available to produce the same discreteenergy photons. This complexity adds to the
challenge of cross section measurements and accurate simulations (discussed in Chapter
3). In addition to PGs emitted immediately following a nuclear interaction, there are
contributions from betadelayed gamma rays. Depending on the halflife of the beta decay,
the gamma rays emitted as a result of these reaction pathways may not be distinguishable
from the PGs.
9
23.
(a) (b)
Figure 4. Production cross sections for PGs resulting from inelastic collisions of protons with 16
O (a)
and 12
C (b). Cross sections are largest at low proton energy. Data from: [45–47].
Detection and imaging of PGs
Detector materials
Particular care must be taken in choosing an appropriate material for detecting PGs.
Each method of detection has characteristic energy, spatial, and time resolution
requirements. For example, collimated systems require excellent spatial resolution and
counting rate capability. A Compton camera design requires good spatial, energy, and time
resolution to perform the necessary reconstruction calculations. If a detection design relies
on timing, such as for background subtraction, excellent timing resolution will be required.
Scintillation detectors are optimal materials for PGI due to their relatively high
stopping power and good time and energy resolutions. Roemer et al. reported a detailed set
of measurements and comparisons with several scintillator materials [48]. Their analysis of
energy and time resolution found sufficient performance amongst several scintillation
materials, with CeBr3 performing best. At clinically relevant gamma energies and count
rates, they found CeBr3 to have an energy resolution of 2.2% and a time resolution of 190
ps fullwidth halfmaximum (FWHM).
In a similar effort, HuesoGonzález et al. performed comparison measurements with
block detectors of LSO (Lu2SiO5:Ce) and BGO (Bi4Ge3O12), both commonly found on PET
detection systems [49]. They focused on optimization for a Compton camera design, which
requires excellent spatial, energy, and time resolution. LSO had the better performance in
their tests, but BGO may be a suitable alternative based on its lower cost.
10
24.
Detection techniques
A sampling of the variety of PGI techniques currently under development is detailed
below. Each method makes a unique attempt at solving the same problem. Namely, to
achieve millimeterrange accuracy in determining the location of the Bragg peak using PGs.
Compton cameras
Multiple efforts are underway to utilize Compton imaging for PG detection [50–54].
This method uses the energy and angle dependence of a Compton scatter interaction to
produce an image, in a technique first described by Everett et al. [55]. This detection
method requires a complex setup of two or more “stages.” If a photon undergoes a
Compton scatter in the first detector and subsequently deposits the remainder of its energy
in a second detector, a cone of possible incident angle can be backprojected into the image
space. The collection of such cones over time allows a reconstruction algorithm to
determine the source origin.
A recent study of such a system was reported by Polf et al. [56]. They produced
some promising results, demonstrating that their Compton camera system can detect
relative shifts of 3 mm and 5 mm in the Bragg peak location. However, the beam current,
and thus the PG production rate, used in their measurements was much lower than that
used at most proton therapy treatment facilities. As a conservative estimate, let us assume
a beam current of 1.0 nA (typical beam currents are 1–3 nA [57]) delivered to the patient
(or 6.2 x 109
protons/s). If a representative treatment field of a daily treatment is 125 cGy
(2.25 x 108
protons), the total beamon time for the dose is 36 ms. Assuming a PG
production rate of 0.1 per proton [58], there will be 6.2 x 108
PGs emitted per second (or
620 MHz). A 100 cm2
detector placed 10 cm away would thus experience a count rate of
~50 MHz. The highest achievable doublescatter count rate for a Compton camera reported
by McCleskey et al. [59] was 5 kHz, well below that needed for clinical delivery rates.
Because the Compton camera is severely limited by count rate (too many counts results in
false coincidences), the authors concede their system is not yet clinically viable. Further
work will continue on the Compton cameras, but until issues of timing and detection
efficiency are solved, the clinical application of such systems will be hindered.
Electrontrack Compton cameras
A variant of the traditional Compton camera, electrontrack Compton cameras
utilize a gas chamber detector to measure the energy and direction of the scattered
electron instead of measuring the energy and angle of the Comptonscattered photon. This
imaging method has also been demonstrated with solidstate detectors [60]. Combining the
energy and direction of the electron with the position and energy deposited by the incident
gamma ray provides sufficient information to determine a cone of incident angles from the
source. Thus, the electrontrack Compton camera only requires one interaction in the
detector to determine the origin direction of a photon.
11
25.
Such a system has been described and analyzed via simulation by Kurosawa et al.
[61]. Kurosawa et al. limited proton beam current to 2.5 pA due to detector dead time
considerations, as opposed to the normal 1 nA beam current used in clinical treatments.
While the concept is interesting, there remains much work to gain feasibility, particularly
due to the limited angular resolution and limited count rate capabilities of current
technology.
Gammaelectron vortex imaging
A method proposed by Kim et al. involves tracing Compton electrons, similar to
electron tracking Compton imaging [62]. An “electron converter stage” converts the
highenergy gamma rays to electrons via Compton scatter. The system then determines the
direction of the electrons using a pair of hodoscopes. Based on the assumption that
electrons scatter in the forward direction, the lines of travel of the electrons are
backprojected to the image space. Like other noncollimated systems, the potential benefit
of this method is higher detection efficiency. However, the authors do not provide specifics
on spatial resolution, although they state that a prototype system is under development for
further study.
PG timing
Protons travel very quickly through tissues, but they still have a finite transit time:
approximately 1–2 ns for a proton with a 5–20 cm range. Because the transit time increases
with range, a timeresolved PG measurement could indicate the range of proton travel. This
PG timing approach was proposed and explored by Golnik et al. [58]. Based on simulations
and initial measurements, the authors suggest that proton range could be determined
within 2 mm using a single scintillation crystal. More work remains to demonstrate the
clinical feasibility of the PG timing method, but the initial report is very promising [57].
Collimated cameras
The most straightforward PGI method is a collimated imaging system. Efforts on
collimated systems for PGI have focused on variations of a singleslit collimator design.
This method provides a onedimensional (1D) profile of PG emission, which would be
most suitable for identifying the PG falloff location by placing the imaging system
perpendicular to the beam axis. The singleslit is a very simple and straightforward concept
to implement, and often uses a knifeedge design to increase the field of view and allow for
image magnification. A prototype knifeedge slit camera tested by Smeets et al. [63]
demonstrated 1–2 mm accuracy in determining the location of a Bragg peak at near
clinically relevant beam currents. Similarly, Bom et al. [64] reported better than 1 mm
accuracy in determining the PG distribution using a singleslit collimator. Realizing the
clinical potential and relative simplicity of the singleslit collimator design, a prototype PGI
system has been developed for clinical application [65].
Options for alternative collimator designs, such as multislit, are limited due to the
properties of PGs. Collimators must be much thicker than those used in diagnostic imaging
12
26.
due to the high photon energy [66]. Therefore, multislit collimators, offering a larger field
of view or potentially greater detection efficiency, are a challenge to design [67].
A high level of neutron background radiation can also limit the effectiveness of a
collimated camera (as well as Compton cameras). Some attempts have successfully
demonstrated adjustments in the technique to overcome the high background limitation.
Using the timing characteristics of the cyclotronproduced proton beam, the PG signature
can be separated from background radiation with sufficient time resolution, thus
improving PGI performance [44]. Timeofflight (TOF) measurements have also been used
to suppress neutron background [68–70].
Conclusion
Due to production rates and proximity to the actual Bragg peak, PGI offers more
potential than PET for successful implementation as a proton range verification method.
However, there has yet to be a study published with results of PGI during patient
irradiations, and thus the clinical effectiveness of PGI remains to be shown. Noncollimated
systems, such as the Compton camera, are plagued by the high count rates experienced
during clinical treatment conditions. Additionally, the detection efficiency of systems
requiring multiple detector interactions for a single event is too low for clinical
consideration. Until solved, the detection efficiency and count rate problems make these
methods infeasible.
The closest system to clinical use is the relatively simple knifeedge slit collimated
system. Multiple experiments have shown the potential to determine PG distributions with
1–2 mm accuracy. As prototype systems are developed and optimized for clinical use, we
may see promising results and approach clinical implementation. However, singleslit
systems are limited to providing 1D information about the PG distribution.
This work proposes a new multislit collimated imaging design to improve upon the
singleslit concept. As the PGI field moves forward in determining the dose distribution in a
patient, a 2D image of PGs will be needed. A Compton camera system would be capable of
providing a 2D distribution of PGs, however the count rate limitation is not likely to be
overcome. The system introduced in this study actualizes the benefits offered by PG
detection, while addressing and overcoming several key challenges associated with PGI.
Finally, unless a straightforward relationship between PG emission yield and dose
deposition can be established, PGI (similar to PET) will have to rely on the comparison of
the measured PG signal with a previously calculated or modeled expectation. Thus, clinical
applicability will require extensive experimental validation of PG yields independent of the
proton beam properties and the irradiated tissue type [22]. Given the range uncertainties
associated with proton therapy and the high costs of building the many new proton therapy
centers across the globe, it is likely that research and development of range verification
methods will continue until an effective solution is found.
13
27.
Chapter 3—A Monte Carlo Study of Prompt
Gamma Production
Background
As computing power has increased per unit cost in recent decades, Monte Carlo
simulation methods have become more prevalent in scientific work. The field of medical
physics, in particular, has extensively used Monte Carlo simulations to model radiation
transport and to study radiation treatment modalities [71]. Monte Carlo simulations offer
an opportunity to study complex or experimentally difficult processes, with a level of detail
and reproducibility not available to the experimentalist. For the PGI application, Monte
Carlo methods would be an invaluable tool for the purposes of
1) optimizing an imaging system for PG detection;
2) identifying and mapping the prompt gamma ray emission profile,
particularly in the inhomogeneous environment of a patient; and
3) providing the means to convert a detected PG profile to a dose distribution.
While proton therapy and proton range in tissue has been studied and validated in
Monte Carlo systems, the production of PGs has only recently become a topic of interest
and simulation due to range uncertainty problems in proton therapy. For dose calculations
of proton therapy, the electromagnetic interactions of protons are well known and can be
reliably simulated [72]. The simulation of PG emission, however, relies on complex reaction
models that were initially developed for highenergy physics applications. While Monte
Carlo methods have been adapted to medical applications successfully, there are still
limitations on the modeling of nuclear excitation and deexcitation. Initial validation
studies of PG emission yields have not shown a consensus between simulated and
experimental results [73]. Furthermore, different Monte Carlo codes produce different
results, as demonstrated in the case of production of positron emitters during proton
therapy [74]. And unfortunately, the production of PGs depends on a far greater number of
reaction channels than for positron emitters due to the complex nuclear physics associated
with the excitation and deexcitation of the nuclei.
As described by Verburg et al. [75], the Monte Carlo nuclear reactions producing
PGs are modeled in three stages:
1) Direct reactions: protons interacting directly with one or two
nucleons of the target.
2) Preequilibrium: protons interact with parts of the nucleus before the
target has reached equilibrium.
3) Compound reactions: energy of the proton is shared statistically
among target nucleons.
14
28.
In the proton energy range of concern for therapeutic use (<200 MeV), all three
reaction stages are relevant and nuclear excitation with subsequent PG emission can occur
in any stage [75]. The complex nature of these interactions, along with the multiple
pathways available (see Table 1), makes it difficult for a theoretical model to accurately
describe the results. This also complicates simulation of these processes. Considering that
the models used in Monte Carlo software were developed without consideration of the PG
productions at lower proton energies, it is understandable that Monte Carlo methods might
not generate accurate simulations.
At the clinical proton energy range, Geant4 uses the AxenWellisch model [76] to
calculate total reaction cross sections. This model uses a general formula for the range of
proton energies from 6.8 MeV to 10 GeV. The model compares relatively well with
experimental data across this large energy range; however, the data set they use [77] is
very sparse at the lower energies. For example, nitrogen cross section data only goes down
to 23 MeV. A more comprehensive set of experimental data for lower proton energies
would allow us to validate this model or make adjustments to it.
The work below adds to evidence that current Monte Carlo systems do not
adequately simulate the PG production during proton therapy, particularly to the level of
accuracy demanded when designing and validating systems for clinical use on patients.
First is a study and comparison of the simulated yield of PGs. Next, the angular distribution
of PGs is simulated and compared with experimental expectations.
PG production cross sections
Methods
To analyze the production rates of PGs, simulations were run using TOPAS, TOol for
PArticle Simulation [78], a userfriendly platform that interfaces with and runs the Geant4
Monte Carlo particle transport package [79]. Beams of varying proton energies were
directed incident on thin targets of carbon and nitrogen. At each proton energy level, 1011
protons were simulated, with resulting photons scored by energy. The resulting PG
emissions were scored and used to calculate a gamma production cross section for each
gamma energy level according to Equation 3:
,Σ = R
I∙N (3)
where:
Σ = cross section (cm2
)
R = total number of gammas produced (specific gamma energy for reaction of interest)
I = total number of protons incident on the target
N = number of target nuclei presented to the beam per unit area (cm2
).
15
29.
Results
Figures 5–7 show gamma production cross sections for carbon and nitrogen targets
for both simulations and experimental data . For the 1.63 MeV gamma produced in 2
nitrogen, the simulated cross section peaks around 20 mb as opposed to the experimental
value which reaches well over 80 mb. The 2.31 MeV gamma from nitrogen shows a similar
peak in simulated and experimental results; however, the simulated cross section results
are shifted to much higher proton energies. The results of the 4.4 MeV gamma in carbon
also show much lower production cross sections in the simulated results than the
experimental measurements.
Figure 5. Comparison of simulation results with experimental data for 1.63 MeV gamma production
during proton irradiation of thin nitrogen target [45,47,80].
These results demonstrate that the Geant4 Monte Carlo simulation package, in this
specific TOPAS configuration, does not adequately simulate PG production. Furthermore,
the lack of discernable pattern across each respective photon energy precludes the
identification of a simple correction or scaling factor that could make the package suitable
for PG simulation. Simulations generate unreliable total gamma counts because of
differences in total reaction production cross sections; this makes them unreliable to
produce and study PG emissions. Additionally, as seen with Figure 6, various proton
energies produce PGs in different locations of the simulated target than would be seen
experimentally. Thus, there are two main components of error: 1) the absolute cross
section and magnitude of PG production, and 2) the relative shape of the yield as a function
2
Because of timing resolution limitations in experimental measurements, the number of photons
counted includes components from PG reactions as well as βdelayed reaction channels, as listed in Table 1.
These reaction channels were also included in the simulation results.
16
30.
of energy (i.e., depth). Until a Monte Carlo system can more accurately model the
production of PGs in tissuerelated materials, simulation data will not be a reliable tool in
assessing and characterizing PG production.
Figure 6. Comparison of simulation results with experimental data for 2.31 MeV gamma production
during proton irradiation of thin nitrogen target [45,47,80].
Figure 7. Comparison of simulation results with experimental data for 4.44 MeV gamma production
during proton irradiation of thin carbon target [45,47].
17
31.
Angular distribution of PGs
Depending on the quantum properties of the excited nuclear level, PG emission is
not generally isotropic [75]. The angular distribution of gamma rays with respect to the
beam direction is given by the Legendre polynomial expansion:
,(θ) P (cosθ), (l even)W = ∑
l=lmax
l=0
al l (4)
where lmax is the smaller of the following two quantities:
1. twice the spin of the decaying state and
2. twice the multipolarity of the gamma ray [81,82].
Measured gamma ray angular distributions produced by protons of up to 13 MeV
are shown in Figure 8 [47]. With the exception of the 6.13 MeV gamma rays from 16
O, the
gamma rays considered in this example have a multipolarity of 2 or less, so that lmax is 4 or
less, and there are at most three terms (l = 0, 2, 4) in the above expansion. In a similar study
using 33 MeV protons, Lesko et al. [45] found the angular distribution of PGs to be more
isotropic than with the 8–13 MeV example. This evidence, provided in Figure 9 for
comparison, suggests an angular distribution dependence on incident proton energy. At
higher proton energies, more nuclear reaction channels are available and there are
contributions from a larger variety of excitation pathways. This explains the different
angular profile seen at higher proton energies, and reemphasizes the complexity of the PG
modeling challenge.
The angular distribution of PGs is important to consider when benchmarking Monte
Carlo simulation results to experimental measurements. As described above, and
corroborated by Verburg et al. [75] with Geant4 and MCNP [83] codes, Monte Carlo
simulations do not produce accurate PG yields within the acceptable range of experimental
data. However, these analyses included only angleintegrated cross sections, which is a
noted limitation of the study because PG emission is not usually isotropic. Robert et al. [84]
compared two Monte Carlo code results for the angular distribution of secondary particles,
showing that both Geant4 and FLUKA [85] codes provide essentially isotropic gamma
emissions. The purpose of their study was to compare the two Monte Carlo packages, but
they do not include any discussion as to the absolute veracity of their simulation results.
The angular distribution of PG emission is equally important as the total PG
production for consideration in actual measurements. Polf et al. [86] described some initial
results using PG detection to quantify the oxygen content in protonirradiated tissue. Their
proposed application is quite promising, but without a correction for the angular
distribution of gammas (which in some cases may vary by a factor of ~2), the precision of
tissue composition determination is severely limited. If a quantitative measure of PGs is to
be performed, an accounting of the angular distribution will be required.
18
32.
Figure 8. Examples of angular distributions of gamma rays. The solid curves are fit with an expansion
in Legendre polynomials of even order through zero for the 14
N case, through 6 for the 16
O case, and
through 4 for the other cases. The gamma ray energies (top to bottom) are 4.44, 2.31, 6.13, 1.63, 1.37,
1.78, and 0.847 MeV. Adapted from [47].
19
33.
Figure 9. Angular distributions for five separate gamma ray transitions resulting from bombardment
of Mylar, Mg, Si, and Fe targets with 33 MeV protons. The solid lines are the results of least squares fit
of Legendre polynomials to the data. Adapted from [45].
Methods
To assess the ability of the Geant4 Monte Carlo package to simulate the PG angular
distribution, a simple simulation was performed with TOPAS using the default physics
models. A spherical scorer (i.e., perfect detector) was placed around a 1 cmthick graphite
target. A beam of protons (109
total) at 13 MeV was directed incident on the target, and the
resulting 4.4 MeV gammas were scored by the detecting sphere and binned in 3°
increments with respect to the beam direction. The proton energy and target material were
chosen to provide a comparison to the experimental data shown in Figure 6.
Given that many researchers falsely assume isotropic PG emission, with some
simulation results reinforcing that misconception [84], an additional analysis was
performed to compare PG distributions as would be detected for isotropic emission with
the experimentally demonstrated angular distributions of emissions.
20
34.
Using the TOPAS simulation, a poly(methyl methacrylate) (PMMA; (C5O2H8)n; and ⍴
= 1.18 g/cm3
) target was irradiated with 160 MeV protons to simulate a typical clinical
proton beam energy setting. The tissueequivalent PMMA target was binned into 0.2
mmthick slices, and the average proton energy in each slice was determined by simulation.
The slice thickness was chosen to avoid significant variation of average proton energy
across any individual target slice. Using data presented in Figure 2 [45–47], cross sections
for production of the 6.129 MeV gamma line from 16
O were calculated via interpolation.
Then, an analytical calculation was performed assuming a detector located at the Bragg
peak location (15.2 cm) and 90° relative to the beam direction. First, the PG emissions were
assumed isotropic and the proton energy in a slice was used in combination with the cross
section at that energy to determine the number of 6.129 MeV gammas produced. Next,
using an interpolation of the data presented in Figures 6 and 7, the angular distribution of
gamma emission was considered along with the angular position of the detector with
respect to the given target slice. The results approximate the difference to be expected
between an actual measurement and a measurement with an assumption of isotropic
gamma emission.
Results
Figure 10 shows the angular distribution of 4.4 MeV gammas resulting from the
simulation of 13 MeV protons incident on a graphite target. The gamma distribution
appears symmetric about a peak at 90°. This is quite different than the experimental results
shown in Figure 8 for the same proton energy and target nucleus. The experimentally
measured gamma distribution shows 90° at the lowest point in the PG emission. In this
case, the simulation model results are less accurate than if the model assumed an isotropic
distribution.
In Figure 11, the blue line represents the 6.129 MeV gamma production in a PMMA
target, assuming that the yield is isotropic. This count total would be measured at the
detector if the gamma production was actually isotropic. The orange line is a more realistic
estimate of the gamma count measured at the detector given the angle of detection. As can
be inferred from Figure 8, at lower proton energies there is a higher yield of 6.13 MeV
gamma as the detection angle moves from 90°. With a detector setup 90° from the Bragg
peak location, the resulting PG distribution measured would be broader than for the
isotropic case—a lower peak near the end of proton range, with some area of higher
magnitude proximal to the peak.
21
35.
Figure 10. Angular distribution of 4.4 MeV PGs (Geant4). This is the result of 109
total protons incident
on a graphite target. 13 MeV p + 12
C.
Figure 11. Comparison of theoretical isotropic PG distribution (blue) and expected gamma
distribution as detected based on angular distribution (orange). Each gamma profile represents the
6.129 MeV gamma production in a PMMA target as would be measured.
22
36.
Conclusion
These results indicate that current Monte Carlo methods do not accurately simulate
the excitation and deexcitation of the nuclei of interest in PGI for proton therapy (C, N, and
O). The reaction cross sections, gamma emission yields, and the angular distribution of
emission for the specific photon energies are not correctly modeled. This important finding
identifies a limitation in the Monte Carlo simulation method—an important tool in studying
the problem at hand. While simulations can still be a valuable asset in designing a system to
detect and image PGs, it is important to understand the limitations of the modeling
packages. Until new models are developed and incorporated into a Monte Carlo simulation
package, the simulation toolkit cannot be used to study the production and location of PG
emissions during proton therapy. This hinders the necessary development of 1) PG distal
falloff verification, and 2) PGtodose profile conversion.
While building a new model of PG emissions is outside the scope of this project, it is
noteworthy to identify this as an issue of importance to the topic. Future work to improve
the simulation modeling of the phenomenon would have a substantial positive impact in
the research community.
23
37.
Chapter 4—A Novel MultiSlit Collimated
Imaging System
Introduction
As discussed in Chapter 2, the most promising method of range verification is PGI
using a collimated imaging system. While there are multiple challenges and limitations to
the use of collimated cameras due to the nature of PGs, the alternatives to collimated
systems have not proven clinically viable [56].
Attempts to improve on the performance of singleslit collimators resulted in the
development of a novel multislit collimator concept [87]. The complete imaging system
(Figure 12) consists of an array of ceriumdoped lutetium oxyorthosilicate (LSO)
positionsensitive scintillation detectors paired with a multiknifeedge slit collimator,
which has been designed, constructed, and characterized at Lawrence Berkeley National
Laboratory (LBNL). Each component of the imaging system is described in the following
sections.
Figure 12. CAD model of imaging system showing 4 x 4 grid of LSO detector modules, tungsten
collimator with individually cut pieces held together with 3D printed plastic, and a proton beam
incident on a plastic target.
24
38.
Collimator design
A multiknifeedge slit collimator has been designed for PGI applications. The 20 x
20 x 7.5 cm tungsten (Hevimet—90% W, 6% Ni, 4% Cu) collimator acts as a coded aperture
system [88–90] for highenergy gamma rays that casts a unique projection for each point in
the imaging plane. An adaptation to the singleslit camera design similar to those used in
previous studies [63,64], the multislit concept was developed to improve detection
efficiency, provide a larger field of view, and offer higher resolution spatial information. To
provide adequate collimation, the material and thickness of the collimator were set such
that 95% of PG emissions would be attenuated if traveling through the thickest part of the
collimator. Knifeedge slits provide a larger field of view than parallel slits and also allow
the opportunity for magnification of the image. Multiple other collimator designs were
considered (for example, a parallel slit design and a fan beam design shown in Figure 13);
however, the minimum septa thickness required to provide proper collimation is limited by
the penetration depth of gammas in the MeV range. Thus, collimation schemes used in
diagnostic imaging require thicker septa, which precludes any significant advantage in
efficiency or spatial resolution over singleslit designs.
Figure 13. Fan beam collimator design modeled in TOPAS. Tungsten slits of 1 mm thickness are
arranged with 2 mm wide openings that get wider as the distance from the object increases. Individual
BGO detectors are placed at the top of the collimator. The design, originally proposed by Andy Haefner
[91], did not offer significant detection efficiency advantages over a single slit collimator.
25
39.
By placing slits at varying angles relative to the central vertical slit, we achieve an
imaging system similar to the 2D coded aperture systems. A linear discriminant analysis
performed on simulated proton paths determined that such geometry has the ability to
discriminate with 10fold higher sensitivity between proton paths with distal ends
separated by 1 mm, as compared with a single slit [87]. After several iterations optimizing
for image contrast, uniformity in imaging sensitivity, and instrument crosscorrelation
sidelobes, the design presented here was determined to be the best candidate for further
investigation (see [87]).
The collimator is composed of 22 individually cut blocks, arranged to provide a
predetermined pattern of knifeedge slits with 2 mm aperture at the center (Figure 14).
The tungsten blocks are held in place by a plastic frame. The collimator pieces are arranged
to imaging sensitivity across the image space that is closer to uniform than an unoptimized
system. Increasing the number of slits provides a greater geometric efficiency, while also
allowing the aperture size to decrease; in our case, 2 mm versus the 3 mm or greater
proposed in other studies. Additionally, the angled slits offer the capability of the
collimator to produce 2D images. The image reconstruction technique and initial
characterizations of the collimator are provided in Chapters 5–7.
Figure 14. Arrangement and dimensions of tungsten collimator pieces.
26
