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Theses and Dissertations
2016
A dosimetric evaluation of the Eclipse and Pinnacle
treatment planning systems in treatment of
vertebral bodies using IMRT and VMAT with
modeled and commissioned flattening filter free
(FFF) fields
Ramzi Ajo Jr
University of Toledo
Follow this and additional works at: http://utdr.utoledo.edu/theses-dissertations
Recommended Citation
Ajo, Ramzi Jr, "A dosimetric evaluation of the Eclipse and Pinnacle treatment planning systems in treatment of vertebral bodies using
IMRT and VMAT with modeled and commissioned flattening filter free (FFF) fields" (2016). Theses and Dissertations. 2023.
http://utdr.utoledo.edu/theses-dissertations/2023
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A Thesis
entitled
A Dosimetric Evaluation of The Eclipse and Pinnacle Treatment Planning Systems
in Treatment of Vertebral Bodies Using IMRT and VMAT with Modeled and
Commissioned Flattening Filter Free (FFF) Fields.
by
Ramzi Ajo, Jr
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Masters of Science Degree in Medical Physics
E. Ishmael Parsai, Ph.D., Committee Chair
David Pearson, Ph.D, Committee Member
Nicholas Sperling, Ph.D, Committee Member
Patricia R. Komuniecki, Ph.D, Dean
College of Graduate Studies
The University of Toledo
May 2016
Copyright 2016, Ramzi Ajo, Jr
This document is copyrighted material. Under copyright law, no parts of this
document may be reproduced without the expressed permission of the author.
An Abstract of
A Dosimetric Evaluation of The Eclipse and Pinnacle Treatment Planning Systems
in Treatment of Vertebral Bodies Using IMRT and VMAT with Modeled and
Commissioned Flattening Filter Free (FFF) Fields.
by
Ramzi Ajo, Jr
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Masters of Science Degree in Medical Physics
The University of Toledo
May 2016
Modern treatment planning systems (TPS’s) utilize different algorithms in computing dose within the patient medium. The algorithms rely on properly modeled
clinical setups in order to perform optimally. Aside from various parameters of the
beam, modifiers, such as multileaf collimators (MLC’s), must also be modeled properly. That could not be more true today, where dynamic delivery such as intensity
modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT)
are being increasingly utilized due to their ability to deliver higher dose precisely to
the target while sparing more surrounding normal tissue. Two of the most popular
TPS’s, Pinnacle (Philips) and Eclipse (Varian), were compared, with special emphasis
placed on parameterization of the dosimetric leaf gap (DLG) in Eclipse. The DLG
is a parameter that accounts for Varian’s rounded MLC leaf ends. While Pinnacle
accounts for the rounded leaf end by modeling the MLC’s, Eclipse uses a measured
parameter. This study investigated whether a single value measured DLG is sufficient
for dynamic delivery.
Using five planning volumes for vertebral body SBRT treatments, each prescribed
for 3000 cGy in 5 fractions, an array of 20 treatment plans was generated using
varying energies of 6MV-FFF and 10MV-FFF. Treatment techniques consisted of 9-
iii
field Step-and-shoot IMRT, and dual-arc VMAT using patient specific optimization
criteria in the Pinnacle TPS v9.8. Each plan was normalized to ensure coverage of
3000cGy to 95% of the target volume. The dose was computed in Pinnacle v9.8, with
the Collapsed Cone Convolution Superposition algorithm and Eclipse v11, with the
Acuros XB algorithm, using a dose grid resolution of 2 mm in both systems. Dose
volume histograms (DVH’s) were generated for a comparison of max and mean dose
to the targets and spinal cord, as well as 95% coverage of the targets and the volume of
the spinal cord receiving 14.5 Gy (V14.5). Patient specific quality assurance (PSQA)
fields were generated and then delivered, using a Varian Edge linear accelerator, to a
4D QA phantom for a gamma analysis and distance to agreement (DTA) comparison.
All Eclipse calculations were made for both measured and optimized DLG parameters.
Calculated vs. measured point dose for the Pinnacle TPS had an average difference
of 2.79 ± 2.00%. Gamma analysis using a 3% and 3 mm DTA had 99/100 fields
passing at > 95%. Using measured values of the DLG in Eclipse, calculated vs.
measured point dose was -4.44 ± 1.97%, and DTA had 33/110 fields passing at >
95%. After an optimization of the DLG in Eclipse, calculated vs. measured point
dose had an average difference of 2.20 ± 2.23%, and DTA with 95/110 fields passing
at > 95%.
This study looked at the performance of the Pinnacle and Eclipse TPS’s, with
special consideration given to the DLG parameterization used by Eclipse. The results
support the idea that a single valued DLG is not sufficient for dynamic delivery. An
optimization of the parameter is necessary to account for the high modulation of
IMRT and VMAT techniques.
iv
I would like to dedicate this thesis to my family.
Acknowledgments
I would like to thank my committee members who provided their expertise and took
the time to be a part of my thesis project. A special thanks goes out to my committee
chair, Dr. E. Ishmael Parsai, whose vast knowledge of the field of Radiation Oncology
Physics proved to be invaluable. I would also like to thank Dr. Nicholas Sperling, for
the countless hours spent assisting me with navigating the technical aspects of the
treatment planning systems used in this study. Thank you Dr. David Pearson and
Dr. Kerry Krugh for agreeing to serve on my committee.
vi
Contents
Abstract
iii
Acknowledgments
vi
Contents
vii
List of Tables
ix
List of Figures
x
List of Abbreviations
xii
Preface
xiv
1 Introduction
1.1
1
Overview of Radiation Therapy . . . . . . . . . . . . . . . . . . . . .
2
1.1.1
Particle Interactions . . . . . . . . . . . . . . . . . . . . . . .
2
1.1.2
The LINAC . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
1.2
Early Dose Computation . . . . . . . . . . . . . . . . . . . . . . . . .
10
1.3
Treatment Planning Systems . . . . . . . . . . . . . . . . . . . . . . .
11
1.3.1
Dose Calculation Algorithms . . . . . . . . . . . . . . . . . . .
12
1.3.1.1
Monte Carlo Simulation . . . . . . . . . . . . . . . .
13
1.3.1.2
Collapsed Cone Convolution Superposition (CCCS) .
13
1.3.2
Acuros XB (AXB) . . . . . . . . . . . . . . . . . . . . . . . .
16
1.3.3
Multileaf Collimator (MLC) Modeling . . . . . . . . . . . . .
18
vii
2 Literature Review
22
3 Methods and Materials
30
3.1
Verification of Dosimetric Leaf Gap for Eclipse . . . . . . . . . . . . .
30
3.1.1
DICOM Files . . . . . . . . . . . . . . . . . . . . . . . . . . .
30
3.1.2
Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
3.1.3
Calculate DLG . . . . . . . . . . . . . . . . . . . . . . . . . .
33
3.2
Plan Selection and IMRT/VMAT Creation . . . . . . . . . . . . . . .
33
3.3
Dose Verification . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
34
3.3.1
37
Optimization of DLG . . . . . . . . . . . . . . . . . . . . . . .
4 Results/Discussion
38
4.0.2
Dosimetric Leaf Gap . . . . . . . . . . . . . . . . . . . . . . .
38
4.0.3
DVH Comparisons . . . . . . . . . . . . . . . . . . . . . . . .
41
4.0.4
Point Dose Measurements . . . . . . . . . . . . . . . . . . . .
46
4.0.5
ArcCheck Verification . . . . . . . . . . . . . . . . . . . . . .
51
5 Conclusion
52
References
55
A 3% at 3 mm DTA Results
57
B DVH Statistics
61
viii
List of Tables
2.1
Experimental validation of D-LBTE solvers in prediction dose . . . . . .
27
4.1
Open field transmission
. . . . . . . . . . . . . . . . . . . . . . . . . . .
38
4.2
Readings for the moving gap. . . . . . . . . . . . . . . . . . . . . . . . .
38
4.3
Average MLC leaf transmission contribution/corrected gap reading . . .
39
4.4
Measured DLG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
40
4.5
Optimized DLG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
4.6
DVH comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
41
4.7
Point dose measurements for measured DLG . . . . . . . . . . . . . . . .
46
4.8
Point dose measurements for optimized DLG . . . . . . . . . . . . . . . .
48
4.9
Distance to agreement results . . . . . . . . . . . . . . . . . . . . . . . .
51
ix
List of Figures
1-1 Two step process of Kerma and dose deposition . . . . . . . . . . . . . .
3
1-2 Linear accelerator (LINAC) . . . . . . . . . . . . . . . . . . . . . . . . .
6
1-3 Treatment head with flattened vs. unflattened beam profiles . . . . . . .
8
1-4 Bremsstrahlung . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1-5 Varian’s HDMLC120 . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
1-6 CT image showing lower Thoracic-spine region. . . . . . . . . . . . . . .
11
1-7 Rounded MLC leaf end . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
1-8 Eclipse parameterization of the rounded MLC leaf end . . . . . . . . . .
19
1-9 Pinnacle model of rounded MLC leaf end . . . . . . . . . . . . . . . . . .
20
1-10 Pinnacle’s region of partial fluence . . . . . . . . . . . . . . . . . . . . .
20
1-11 Uncorrected and corrected fluences . . . . . . . . . . . . . . . . . . . . .
21
3-1 WPD1 water phantom from IBA Dosimetry. . . . . . . . . . . . . . . . .
31
3-2 CNMC Instruments Inc Model 206 dosimetry electrometer . . . . . . . .
32
3-3 PTW Model 30013 waterproof ion chamber . . . . . . . . . . . . . . . .
32
3-4 CNMC Instruments Inc Model DBT-100T digital barometer/thermometer
32
3-5 Sun Nuclear ArcCheck setup . . . . . . . . . . . . . . . . . . . . . . . . .
35
3-6 Exradin A16 micro ion chamber placed inside ArcCheck . . . . . . . . .
36
3-7 SNC Patient fluence analysis . . . . . . . . . . . . . . . . . . . . . . . . .
37
4-1 DLG extrapolation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
39
4-2 Modulation factors vs. percent difference in measured dose . . . . . . . .
40
x
4-3 DVH of 10FFF IMRT for measured DLG. . . . . . . . . . . . . . . . . .
42
4-4 DVH of 10FFF IMRT for optimized DLG. . . . . . . . . . . . . . . . . .
42
4-5 DVH of 10FFF IMRT for measured DLG. . . . . . . . . . . . . . . . . .
43
4-6 DVH of 10FFF IMRT for optimized DLG. . . . . . . . . . . . . . . . . .
43
4-7 DVH of 6FFF VMAT for measured DLG. . . . . . . . . . . . . . . . . .
44
4-8 DVH of 6FFF VMAT for optimized DLG. . . . . . . . . . . . . . . . . .
44
4-9 DVH of 10FFF VMAT for measured DLG. . . . . . . . . . . . . . . . . .
45
4-10 DVH of 10FFF VMAT for optimized DLG. . . . . . . . . . . . . . . . . .
45
4-11 Predicted Pinnacle vs delivered dose . . . . . . . . . . . . . . . . . . . .
47
4-12 Predicted Eclipse vs delivered dose with measured DLG
. . . . . . . . .
47
4-13 Predicted Eclipse vs delivered dose with optimized DLG . . . . . . . . .
48
4-14 Isodose comparison for patient 1 . . . . . . . . . . . . . . . . . . . . . . .
49
4-15 Isodose comparison for patient 4 . . . . . . . . . . . . . . . . . . . . . . .
50
xi
List of Abbreviations
AAA . . . . . . . . . . . . . . . . . . . . . . Analytical Anisotropic Algorithm
ABR . . . . . . . . . . . . . . . . . . . . . . American Board of Radiology
BED . . . . . . . . . . . . . . . . . . . . . . Biological Equivalent Dose
BSF . . . . . . . . . . . . . . . . . . . . . . Backscatter Factor
CCC . . . . . . . . . . . . . . . . . . . . . . Collapsed Cone Convolution
CPU . . . . . . . . . . . . . . . . . . . . . . Central Processing Unit
CT . . . . . . . . . . . . . . . . . . . . . . . Computed Tomography
DC . . . . . . . . . . . . . . . . . . . . . . . Direct Current
DICOM . . . . . . . . . . . . . . . . . . . Digital Imaging and Communications in Medicine
DLG . . . . . . . . . . . . . . . . . . . . . . Dosimetric Leaf Gap
DNA . . . . . . . . . . . . . . . . . . . . . . Deoxyribonucleic Acid
DVH . . . . . . . . . . . . . . . . . . . . . . Dose Volume Histogram
EBRT . . . . . . . . . . . . . . . . . . . . External Beam Radiation Therapy
EM . . . . . . . . . . . . . . . . . . . . . . . Electromagnetic
FFF . . . . . . . . . . . . . . . . . . . . . . Flattening Filter Free
HT . . . . . . . . . . . . . . . . . . . . . . . Hydrogen Thyratron
HU . . . . . . . . . . . . . . . . . . . . . . . Hounsfield Units
IMRT . . . . . . . . . . . . . . . . . . . . . Intensity Modulated Radiation Therapy
KERMA . . . . . . . . . . . . . . . . . . Kinetic Energy Released in the Medium
kVp . . . . . . . . . . . . . . . . . . . . . . . kiloVolts Peak
LBTE . . . . . . . . . . . . . . . . . . . . Linear Boltzmann Transport Equation
LINAC . . . . . . . . . . . . . . . . . . . Linear Accelerator
MC . . . . . . . . . . . . . . . . . . . . . . . Monte Carlo
xii
MeV . . . . . . . . . . . . . . . . . . . . . . Mega-Electron Volts
MIM . . . . . . . . . . . . . . . . . . . . . . Medical Imaging Management
MLC . . . . . . . . . . . . . . . . . . . . . . Multileaf Collimator
MV . . . . . . . . . . . . . . . . . . . . . . . MegaVoltage
PACS . . . . . . . . . . . . . . . . . . . . . Picture Archiving and Communication System
PBC . . . . . . . . . . . . . . . . . . . . . . Pencil Beam Convolution
PDD . . . . . . . . . . . . . . . . . . . . . . Percent Depth Dose
PET . . . . . . . . . . . . . . . . . . . . . . Positron Emmission Tomography
PFN . . . . . . . . . . . . . . . . . . . . . . Pulse-Forming Network
RPC . . . . . . . . . . . . . . . . . . . . . . Radiological Physics Center
QA . . . . . . . . . . . . . . . . . . . . . . . Quality Assurance
SAR . . . . . . . . . . . . . . . . . . . . . . Scatter-Air-Ratio
SBRT . . . . . . . . . . . . . . . . . . . . . Stereotactic Body Radiation Therapy
SMD . . . . . . . . . . . . . . . . . . . . . . Source-to-MLC Distance
SRS . . . . . . . . . . . . . . . . . . . . . . . Stereotactic Radiosurgery
SSD . . . . . . . . . . . . . . . . . . . . . . Source-to-Surface Distance
TAR . . . . . . . . . . . . . . . . . . . . . . Tissue Air Ratio
TERMA . . . . . . . . . . . . . . . . . . Total Energy Released per Unit Mass
TLD . . . . . . . . . . . . . . . . . . . . . . Thermoluminescent Dosimeter
TMR . . . . . . . . . . . . . . . . . . . . . Tissue-Maximum-Ratio
TPR . . . . . . . . . . . . . . . . . . . . . . Tissue-Phantom-Ratio
TPS . . . . . . . . . . . . . . . . . . . . . . Treatment Planning System
VMAT . . . . . . . . . . . . . . . . . . . . Volumetric Modulated Arc Therapy
xiii
Preface
This thesis is original, unpublished work by the author, R. Ajo. It was completed
under the advisement of the division of Medical Physics at the University of Toledo
in fulfillment of the requirements for the Masters of Science in Biological Sciences
(MSBS) with a specialization in Therapeutic Medical Physics.
xiv
Chapter 1
Introduction
The goal of this work will be to investigate and evaluate the performance of two
treatment planning systems (TPS’s) with modeled and commissioned flattening filter
free (FFF) fields, Pinnacle (Philips) and Eclipse (Varian). Particular attention will be
given to the parameterized value of the dosimetric leaf gap (DLG) used in the Eclipse
TPS that accounts for the rounded leaf ends of the multileaf collimator (MLC). The
Pinnacle model used in this study has been fully commissioned and in clinical use for
more than 7 years, while the Eclipse model has only been partially commissioned for
select energies and has not been used clinically. The evaluation will ultimately compare the two dose computation algorithms, Pinnacle’s Collapsed Cone Convolution
Superposition (CCCS) and Eclipse’s Acuros XB (AXB), and predicted vs. directly
measured values will be expressed. Dose volume histogram (DVH) statistics between
the two planning systems will also be reviewed and analyzed.
Dosimetric comparisons will be made using predicted values, with measurements
serving as the gold standard. In order to truly push the capabilities of the TPS’s,
intensity modulated radiation therapy (IMRT) and volumetric modeled arc therapy
(VMAT) plans involving vertebral body lesions will be used. The tissue-bone interface
will provide a good medium for which to perform the evaluation, and sparing of the
spinal cord will provide feedback as to how well the MLC has been modeled by the
1
treatment planning algorithms, particularly Eclipse’s Acuros XB. Varian recommends
a measurement to determine the DLG parameter for use in the Eclipse TPS, however
that approach does not work well for dynamic delivery, such as IMRT and VMAT.
1.1
Overview of Radiation Therapy
1.1.1
Particle Interactions
Radiation therapy has become an increasingly popular modality in the fight against
cancer. Lesions that are not suitable for resection are often treated with non-invasive
radiation therapy. This has become a popular choice for both physicians and patients
due to the ease of the procedures. Whereas surgery included extensive inpatient time
followed by an often lengthy recovery time, radiation therapy is quick and typically
an outpatient procedure. The patient walks into the cancer center and is treated in
as little as twenty minutes. With the introduction of stereotactic body radiotherapy
(SBRT) to the array of radiation treatment options, patients can complete an entire
course in less than five sessions. In fact, the patient setup is the most time consuming part of the process. Once the patient has been set up, the actual delivery takes
around five to ten minutes. These highly localized high dose treatments have become
the go-to option for select sites within the body, such as vertebral body lesions. Dynamic delivery of treatment, in the form of intensity modulated radiation therapy
(IMRT) and volumetric modulated arc therapy (VMAT), helps to cover the target
while sparing more healthy tissue and critical structures. This is especially important
in cases involving vertebral bodies where dose to the spinal cord must be minimized.
Until around 1950, most external beam radiotherapy (EBRT) used x-rays of superficial and orthovoltage range. At that point higher energy machines were under
development, and cobalt-60 units were becoming popular enough to signal a decline
of the conventional kilovoltage machines. These treatment machines never went away
2
though, and even with today’s megavoltage beam generating machines they still find
their usefulness in the treatment of some superficial lesions [1].
The clinical machine used to treat most cancers via radiotherapy is the linear
accelerator (LINAC). The fight against malignant cancer begins with ionizing radiation targeting the deoxyribonucleic acid (DNA) of the cancer cells, which is delivered
precisely by today’s LINACs. The ionizing radiation deposits dose within the cancer
cells, damaging their DNA and preventing them from proliferating.
Figure 1-1: The KERMA has its max dose at the skin and remains linear
while absorbed dose experiences a buildup before equilibrium
with KERMA. Note the absorbed dose is always at a greater
depth than the KERMA due to the finite range of electrons [2].
Dose deposition is a two step process, beginning with a transfer of energy to
electrons known and the kinetic energy released in the medium (KERMA). The dose
is then deposited at a different location than the KERMA. This is due to the finite
range of electrons and can be seen clearly in Figure 1-1. The transfer and absorption
of dose is driven by the following processes:
3
1. Photoelectric Effect
2. Coherent and Incoherent Scattering (or Compton Effect)
3. Pair Production
Coherent scattering is a process in which all the energy is scattered and none is
converted into kinetic energy, therefore we do not care about this effect when considering absorbed dose and consider it irrelevant. Incoherent scattering however, also
called Compton scattering, is quite relevant in the MV range of LINACs. Compton
scattering occurs when a free electron scatters independently. “Some energy is scattered and some is transferred to kinetic energy. It is the most important interaction
mechanism in tissue-like materials”[3].
In order to understand the Compton process, the quantum nature of radiation
must be considered. Imagine the electromagnetic wave as a stream of photons with
energy hν and momentum hν/c. The photon will collide with a free electron, setting
it in motion with an energy E at an angle φ. The scattered electron carries part of
the energy while the rest is carried by the photon, which was also scattered at an
angle θ with an energy hν 0 . The energy of the collision is conserved, thus we can say:
hν = hν 0 + E
(1.1)
Unlike photoelectric absorption the Compton process is almost independent of
atomic number, however its probability does also decrease as incident energy increases.
As energy increases, the average fraction of energy transferred to kinetic energy per
collision also increases [3].
The last process is called pair production, and only occurs at energies above 1.022
MeV. It occurs when the photon passes near the nucleus of an atom and is subjected
by its strong nuclear field. When this happens, the photon can transform into a
4
positive and negative electron pair. The energy of the photon is converted into mass
based on:
E = mc2
(1.2)
Since one electronic mass is 0.511 MeV, and two particles are formed in this
process, the threshold energy of the photon must be 2 x 0.511 MeV = 1.022 MeV. Since
the two particles (electron and positron) carry opposite charges, no net electronic
charge is formed. Therefore any energy greater than 1.022 MeV is shared between
the electron and positron, thus we can say:
hν − 1.022 = E+ + E−
(1.3)
where E+ and E− are the kinetic energies of the positron and electron, respectively.
As the positron travels through matter it excites and ionizes just as an electron would
under similar conditions. Once it comes to rest, however, it combines with a free
electron and is annihilated to produce two photons of energy 0.511 MeV. This is an
example of mass being converted into energy, the opposite of what Eq. 1.2 describes.
The two photons are set out in opposite directions from the site of annihilation, and
since no net charge is created or destroyed, electric charge is conserved. In the rare
case that the positron is captured by an electron before coming to rest, its kinetic
energy is added to the energy of the photons. Another possibility, although not as
common, is triplet production, which is similar to pair production in all but two ways:
• The interaction occurs in the field of the electron rather than the field of the
nucleus, hence a third particle (the original electron) appears.
• The threshold energy is 2.04 MeV
The probability of pair production occurring increases rapidly as energy increases
5
above 1.022 MeV. This means that high energy photons are more easily stopped by
pair processes than lower energy photons. Thus for instances where pair production
is the main attenuation process, high energy beams are less penetrating. This is
the only process whose probability increases with energy. The probability also varies
approximately as Z 2 per atom and Z per unit mass [3].
1.1.2
The LINAC
A LINAC accelerates electrons to high energies by way of high-frequency electromagnetic (EM) waves. The electrons are then used to treat shallow or superficial
lesions. For deep-seated tumors, the electrons exiting the LINAC can strike a target to produce x-rays that have greater penetration and higher skin-sparing qualities
than electrons. Although there are several designs of LINACs, those used in radiation therapy utilize an electron acceleration method of either traveling or standing
waves. Standing wave designs tend to be more efficient and, while they are also more
expensive than their traveling wave counterparts, are utilized in most modern-day
LINACs. Figure 1-2 illustrates the typical design of a medical LINAC.
Figure 1-2: Block diagram of a typical medical linear accelerator [1].
Direct current (DC) is provided by the high voltage power supply to a modulator assembly, which contains a pulse-forming network (PFN) and hydrogen thyratron
6
(HT). The PFN acts as capacitor and is charged by the power supply, then discharged
by the HT which acts as a high current switch. The pulses are used to power filaments
in both the magnetron or klystron and the electron gun. The pulsed microwaves generated by the magnetron or amplified by the klystron are injected into the waveguide
system such that they will enter the accelerator tube at the precise instant as electrons
from the electron gun.
The accelerator tube is composed of a highly evacuated vacuum copper tube
with an interior consisting of copper discs of variable spacing and gap. As electrons
enter this tube with initial energy around keV, they interact with the EM field of
the microwaves, gaining energy in an acceleration process. As the now high-energy
electrons exit the accelerator tube, they form a 3 mm diameter pencil beam. For lowenergy LINACs (≤ 6MV) the electrons exit straight out from the accelerator tube.
However, in high-energy LINACs (> 6MV) the electrons must first travel through
a bending magnet of either 90◦ or 270◦ before entering the treatment head. The
bending magnet allows for the higher energies and the total length of the unit will
be based on the highest energy output of the LINAC. It also allows more focusing of
the electron beam prior to hitting the target, resulting in beams of < 1 mm in focal
diameter.
7
Figure 1-3: Treatment head assembly illustrating flattened (left) and unflattened (right) beams [4].
The treatment head is shown in Figure 1-3 and illustrates both flattened and
unflattened beam profiles. It can easily be seen that the only difference is the presence
of a flattening filter. Historically, it has always been beneficial to have flattened fields.
This helped achieve uniform dose distributions and also greatly helped in performing
dosimetry. However with the advent of IMRT, uniformity can be easily achievable
even with flattening filter free (FFF) beams. The added benefit to FFF beams is
their high dose rate, which enables much faster treatment deliveries.
Within a thick shell of high-density shielding materials like lead, tungsten, or a
combination of the two, lies the inner workings of the treatment head. The pencil
beam of electrons first strikes the target (made up of high-Z material), results in the
generation of high energy photons via bremsstrahlung, which is the main source of
x-rays generated by LINACs. Bremsstrahlung, which means braking radiation and is
illustrated in Figure 1-4, occurs as an electron passes close to a nucleus of high-Z material. The electron is attracted to the nucleus just enough to cause it to slow down
8
and change direction. Due to the change in momentum of the electron, in accordance with conservation laws, the energy lost is emitted in the form of characteristic
radiation called bremsstrahlung. Most of the useful radiation used for therapeutic
purposes is formed by this process.
Figure 1-4: Depiction of the occurrence of bremsstrahlung [5].
The photons then travel through a conical hole in the primary collimator (made
of high-Z material), then through a tungsten flattening filter. For high dose rate FFF
fields the flattening filter is simply moved out of the way. Next the beam passes
through a fixed ion chamber that monitors output, beam flatness, and symmetry.
Below the ion chamber is a light localizer used for patient setup. It is aligned to the
radiation field and is retracted once patient setup is complete. The beam then passes
through the secondary collimator system which consists of two sets of high-Z jaws,
inline and cross-line. The secondary collimation system is responsible for setting the
field size. With respect to the Varian Edge, the maximum static field size is 40 cm x
22 cm. The last fixed component is the multi-leaf collimator (MLC) assembly, which
shapes the field for treatment. During IMRT and VMAT treatments the MLCs are
9
constantly moving around in order to modulate the intensity of the fields. Each MLC
configuration is known as a control point. The High-definition 120 MLC of the Varian
Edge, shown in Figure 1-5, consists of 32 pairs of leaves that each project 2.5 mm
at isocenter, with another 28 pairs projecting 5 mm at the peripheral. The central,
smaller leaf width provides improved target coverage for both IMRT and VMAT
treatment plans, with a much greater improvement in IMRT than in VMAT [6].
Figure 1-5: The HDMLC 120 of the Varian Edge Radiosurgery Suite [7].
1.2
Early Dose Computation
Before modern computers, dose computations were performed by hand. CT data
was not available at the time, thus patients were set up using solder wires, pantographtype contour plotters, and electromechanical devices. Beam profiles were known from
irradiation of water, and simple corrections were made based on depth and attenuation
for topographic irregularities (air gaps) and tissue inhomogeneities. The calculations
utilized tissue-maximum ratio (TMR) charts and large tables of factors such as tissueair ratio (TAR), backscatter factor (BSF), scatter-air ratio (SAR), and more to make
the corrections. Methods like effective attenuation as well as effective SSD were
commonly used to make these adjustments.
10
1.3
Treatment Planning Systems
Treatment planning starts with the acquisition of patient data, typically in the
form of computed tomography (CT), shown in Figure 1-6. CT machines provide a
crucial component needed in the computation of dose in the human body, electron
density.
Figure 1-6: CT image showing lower Thoracic-spine region.
The reconstruction of images by CT is mathematical process using complex algorithms to generate Hounsfield Units (HU), which are a function of electron density and
related to attenuation coefficients [1]. CT numbers range from -1000 (air) to +1000
(bone), with 0 representing water. CT scanners are calibrated with a reference to
water, thus HU’s are determined using:
HU =
µtissue − µwater
× 1000
µwater
(1.4)
where µ is the linear attenuation coefficient. One Hounsfield unit represents a
change of 0.1% in the attenuation coefficient of water [1].
With respect to radiation therapy treatment planning, the information provided
11
by CT machines allows for the delineation of target volumes and surrounding structures relative to the external contour, and provides quantitative data (CT numbers)
for tissue heterogeneity corrections. Often times there will be positron emission tomography (PET) CT’s or magnetic resonance imaging (MRI) scans used to help the
physician identify the targets. These scans are fused to the original CT, also known
as the planning CT, before the targets are contoured in. Once the Planning CT has
been generated and contains the targets to be treated, it is transferred to the treatment planning system (TPS). At this point, if not done already, the critical structures
will be contoured in by the planner. Contours can be drawn using the TPS itself or
another program such as Medical Imaging Management (MIM).
Next begins the planning stage of the treatment plan, which consists of the following:
• Prescription designation
• Energy selection
• Field orientation
• Couch/table shifts
• Criteria optimization
• Dose computation
1.3.1
Dose Calculation Algorithms
Currently, calculations for dose deposition in radiotherapy are exclusively performed using advance algorithms. With the aid of increasingly faster computer processors, the algorithms continue to become more powerful and gain the ability for
more accurate calculations. Speed is crucial in the clinical setting, where patients are
12
relying on quickly developed and implemented treatment plans. Although speed is
important, accuracy should never be compromised to achieve it.
1.3.1.1
Monte Carlo Simulation
Monte Carlo (MC) simulation is the ultimate resource for dose computation. The
method has been around for more than 200 years to solve mathematical problems
using numerical integration of functions [8]. MC is a method that stochastically solves
the Linear Boltzmann Transport Equation (LBTE), which describes the macroscopic
behavior of radiation particles as they travel through and interact with matter. The
histories of millions of photons and secondary electrons are traced and their dose
deposition is calculated based on the interactions in matter. As stated earlier it is
the most accurate method of dose computation, however, it requires the greatest
processing time. Fortunately, many calculation algorithms use pre-calculated MC
dose kernels. Energy deposition kernels describe the spatial distribution of the energy
imparted by a photon/electron interacting at a point in a medium. MC works so well
because it uses basic physics interaction probabilities (sampled via selection of random
numbers) to determine the fate of the representative particles.
1.3.1.2
Collapsed Cone Convolution Superposition (CCCS)
A true three-dimensional dose computation engine that accounts for inhomogeneities for both primary and secondary scatter is Pinnacle’s CCCS from Philips
Medical Systems. This method has the ability to accurately predict dose distributions in areas where electronic equilibrium may be perturbed, such as tissue-bone
interfaces often seen in vertebral body treatments. IMRT is typically a time consuming computation however, using Pinnacle’s Delta Pixel Beam the accuracy of CCCS
can be maintained while improving the speed of optimization.
The CCCS algorithm is model based, meaning it accounts for the effects of beam
13
modifiers, external contours, and inhomogeneities, rather than simply trying to use
correction methods. Pinnacle’s model consists of four parts [9].
First, the incident energy fluence is modeled as a two-dimensional array, representing the radiation exiting the accelerator head. It starts with a uniform plane of
fluence leaving the top of the accelerator head, and is then adjusted for the accelerator
head itself, the flattening filter, and any beam modifiers such as wedges or blocks.
• The “horns” produced by the flattening filter are modeled by inverting a cone
within the beam and removing it from the distribution.
• Off-axis scatter is modeled using a 2D Gaussian function as the scatter source.
• Geometric penumbra is modeled by convolving the array with a blurring function.
• The shape of field produced by a block or MLC configurations is simply cut out
of the array.
• The fluence through wedges and compensators is attenuated by the corresponding thickness of the modifier. Beam hardening is accounted for in fixed modifiers
by storing a radiological depth array.
The second part of the process involves the energy fluence being projected through
the planning CT. It is attenuated using mass attenuation coefficients stored in a threedimensional lookup table. The coefficients are functions of:
• Density
• Radiological depth
• Off-axis angle
14
In order to account for changes in the energy spectrum at different locations
throughout the beam, the mass attenuation coefficient lookup table is a sum of several
mono-energetic tables. The Total Energy Released per unit Mass (TERMA) volume
is computed using ray-tracing, in which a given ray’s direction is determined based
on the position of the source and location of the incident fluence plane.
Next is the three-dimensional superposition of the TERMA with an energy deposition kernel. The kernels are generated through Monte Carlo simulation and represent
the spread of energy from the primary interaction site throughout the volume. The
superposition is performed using ray-tracing in a similar fashion to that of the projection of energy fluence. Radiological distance along the ray path accounts for the
effects inhomogeneities in regards to scatter in all directions. Multiple beams are
computed independently and the 3D distribution is produced using corresponding
beam weights.
Lastly, the electron contamination is modeled with an exponential falloff which is
added to the dose distribution after the photon dose is computed.
Pinnacle also incorporates an approach known as Adaptive Convolution Superposition in order to decrease computation time. This method varies the resolution of the
dose computation grid based on the the curvature of the TERMA and dose distribution. Regions of high curvature, which represents low dose uniformity, are computed
using intermediate points, whereas regions of low curvature, which represent more
uniformly distributed dose, are computed using a coarse grid and then interpolated.
This method has been shown to reduce computation time by a factor of 3.
Delta Pixel Beam dose optimization used in IMRT is a hybrid of CCCS and a
finite-sized pencil beam (PB) algorithm. It essentially optimizes the treatment to
an intermediate solution using the PB algorithm, then CCCS takes over and fully
computes the dose per beam. The base CCCS dose remains for the rest of the
optimization and is only updated based on perturbations as determined by the PB
15
algorithm, which is based on the same modeling process as Pinnacle [9].
1.3.2
Acuros XB (AXB)
The basis of Eclipse’s AXB dose computation algorithm was to address both accuracy and speed in EBRT. Inhomogeneities are a significant source of complication
in most computation algorithms, and AXB uses a complex and sophisticated technique to predict dose distributions. It solves the Linear Boltzmann Transport Equation (LBTE) and directly accounts for inhomogeneities, with accuracy comparable to
Monte Carlo methods.
The LBTE is an equation that describes the macroscopic behavior of radiation
particles as they travel through and interact with matter, assuming that the particles
only interact with the matter they are traversing and not with each other. Thus for
a given volume, the solution to the LBTE would give an exact description of the
dose within that volume. However, since analytical solutions of the LBTE are only
possible for a few simplified problems under a specific set of conditions, it is solved
in a non-analytical manner [10].
Two methods are available to solve the LBTE:
• Monte Carlo
an indirect method of stochastically obtaining the solution to the LBTE.
• AXB
explicitly solving the LBTE using numerical methods and discretization
The two methods are said to be convergent, meaning that with sufficient time
they will converge on the same solution. AXB patient transport and dose calculation
consists of four discrete steps which proceed as follows [10]:
16
First is the transport of source model fluence into the patient. Here the machine
source is modeled as an external source and ray tracing calculates the uncollided
photon and electron fluence in the patient.
The second and third steps discretize in space, angle, and energy, then iteratively
solve the LBTE.
In the last step, the dose in any voxel is generated through applying an energy
dependent fluence-to-dose response function to the local energy dependent electron
fluence in that voxel. Two dose reporting options are supported: dose-to-water (DW )
and dose-to-medium (DM ). When DM is calculated the response function is based
on the material properties of the voxel, whereas when DW is calculated the response
function is based on water.
The process of dose calculation is dependent on a material map of the imaged patient. While convolution/superposition algorithms handle inhomogeneities as density
based corrections applied directly to dose kernels calculated in water, AXB explicitly models the interactions of the radiation with matter. This requires, in addition
to density, the interaction cross-section of each material in which particle are transported through. Eclipse provides AXB with mass density as well as material type
for each voxel of the image grid. Material libraries include five biological materials
(lung, adipose tissue, muscle, cartilage, and bone) and 16 non-biological materials.
The maximum supported density is 8.0 grams/cm3 (steel) [10].
Calculation options for AXB are summarized below:
• Calculation grid voxel size - ranges from 1 to 3 mm.
• Dose reporting mode - DW or DM
• Plan dose calculation - used for decreasing computation time of IMRT and
VMAT plans
17
• Material specification - automatic or manual material assignment
• Configuration - uses same source model as AAA, thus no additional beam data
needed
AXB addresses accuracy and speed requirements for treatment planning in radiation therapy, especially for techniques such as IMRT and VMAT. Accuracy is
comparable to Monte Carlo methods for the full range of x-ray energies used by today’s LINACs. Validation has been performed to assure this accuracy in simulated
patient geometries with excellent results [10].
1.3.3
Multileaf Collimator (MLC) Modeling
A fairly new challenge to treatment planning systems is the modeling of the MLC,
particularly the rounded leaf end of Varian’s HDMLC120. The rounded ends allow
for some leakage, and another difference in TPS algorithms is the way they account
for that leakage. Figure 1-7 illustrates Varian’s rounded MLC leaf end.
Varian’s rounded MLC leaf end
Figure 1-7: MLC leaf with rounded end showing a portion of the leaf material in the radiation field. Some leakage will occur due to less
attenuation of the beam.
18
Eclipse Model
Figure 1-8: Eclipse models the rounded leaf end as a sharp flat edge, and
then corrects for it by introducing a gap that accounts for leakage
known as the dosimetric leaf gap.
As shown in Figure 1-8, the result of Eclipse’s model allows for the full fluence
of the radiation field to pass through the DLG. Pinnacle handles the rounded leaf
ends a bit differently, by considering an exponential decay of the fluence by means of
attenuation. Figure 1-9 shows the method in which the Pinnacle model accounts for
the rounded leaf ends. The result is a region of partial fluence that passes through
the edges of the leaves, as depicted by Figure 1-10.
19
Pinnacle Model
Figure 1-9: Pinnacle models the rounded leaf ends of the MLC’s as a function
of source-to-MLC (SMD) distance, MLC thickness, and effective
radius of the rounded ends.
Pinnacle Model
Figure 1-10: The full fluence is exponentially attenuated at the leaf ends
giving way to the region of partial fleunce,
20
Fluence illustration Uncorrected vs. Corrected
Figure 1-11: Uncorrected fluence (left), Eclipse model correction allowing full
fluence at outer edges of the field (center), Pinnacle’s correction
modeling a region of partial fluence (right).
21
Chapter 2
Literature Review
Accuracy and speed are the two key factors that play a role in dose calculation.
The two main types of dose calculation to focus on are correction based and model
based. Correction Based is a type of empirical dose calculation which interpolates
and extrapolates dose from measurement in water such as percentage depth dose
(PDD) for different field sizes at a certain source to surface distance (SSD) [11].
In order to make an algorithm successful we must test in both homogenous and
heterogeneous mediums. When looking at the two mediums homogenous is in water
and heterogeneous can be bone, lungs, etc. Three concepts make a homogenous
medium accurate and include: Tissue air ratio (TAR), Tissue phantom ratio (TPR),
and Tissue maximum ratio (TMR). The Clarkson technique and IRREG are two
examples of this algorithm that are still used today for manual dose calculation. For
correction based the heterogeneous system is not as accurate due to lateral scattering
when the beam hits the various media.
Model based is when a particle beam interacts with media at a point, releases
energy and then is deposited or scattered away from primary site. During this time
it may create secondary photons and electrons, releasing energy in the scattering
path [11]. This process is simplified by using a convolution equation which convolves
the primary photon energy fluence referred to as the total energy released per unit
22
mass (TERMA) by way of a kernel that describes the contribution from scattering
photons and electrons. If you look at the inhomogeneity of the media, this method is
called convolution-superposition. When applying the kernel for the treatment plans,
you can use three sub-difference algorithms which include Pencil Beam Convolution
(PBC), Analytical Anisotropic Algorithm (AAA) and Collapsed Cone Convolution
(CCC). When comparing homogenous vs heterogeneous, there is minimal difference
in accuracy with homogenous. However, with heterogeneous media the actual path
length is replaced with radiological path length to take into account the variation
in electron density from water. “Accuracy is determined by how well the kernels of
these algorithms can simulate the actual scattering. In PBC the lateral scattering
is considered to be homogenous, and the inhomogeneity correction only happens in
the longitudinal direction which is accounted for by using the equivalent path length
converted from mass attenuation (electron density of the media)” [11]. In AAA and
CCC both consider the heterogeneous effect in the longitudinal direction as well as
the lateral one. AAA utilizes Gaussian functions to describe the mean effect in four
lateral directions (± x and ± y). In CCC the kernel is replaced by a certain number of
discrete elements and the mean for all elements are used. Most research shows CCC is
more accurate than AAA when inhomogeneity corrections need to be applied. They
found the rationale to be that CCC handles the lateral scattering better than AAA
in heterogeneous media.
The Monte Carlo algorithm has been used as a benchmark to check the accuracy
of dose calculations. The process begins with the initiation of seed generation into the
target to simulate the actual process. ”Radiation beam travels through the accelerator gantry head including the collimator system. Collimated beam particles from the
gantry head travel through and distribute dose in patient’s body” [11]. The speed of
Monte Carlo dose computation is a slow process, however, development of computer
CPU power greatly enhances the speed. The Monte Carlo method is very accurate,
23
which has lead to investigation of other methods for dose calculation. Dose calculation accuracy is best executed when you perform measurements then compare the
measured to calculated dose in both homogenous and heterogeneous media. “Many
works have been done to evaluate the above-mentioned dose calculation algorithms,
and the hierarchy of accuracy is as follows: Monte Carlo algorithm > Acuros XB >
CCC > AAA > PBC > Correction based methods. With Monte Carlo and Acuros
XB algorithms, you can expect a close to 100% accuracy of dose calculation if time
permits” [11].
Similar to Monte Carlo, Acuros XB will simulate all the physical processes that
the beam involves instead of generating one by one in simulation process. Acuros
XB uses the Boltzmann transport equation (BTE), which describes the process of
ionizing particles interacting and travelling through matter. The Linear Boltzman
transport equation (LBTE) is a form of the BTE which assumes the particles do not
interact with each. Equations are solved using numerical methods, which is much
faster than Monte Carlo but still comparable in accuracy.
A demonstration of solving the LBTE was originally performed using the prototype software Attila, a general grid-based solver [12]. Another form of LBTE is
deterministic LBTE (D-LBTE), which discretizes the variables with regards to energy and space into an infinite grid. It can simulate similar results from the MC
model. The recently developed new deterministic solver, Acuros XB, in the Eclipse
treatment planning system also approximates the LBTE solution. The D-LBTE first
calculates the fluence of the electron angular then the dose is calculated using electron
energy cross sections and density of the material.
The use of the Boltzmann Equation proceeds as follows [12]:
b · ∇Φ
~ γ + σ γ Φγ = q γγ + q γ ,
Ω
24
(2.1)
b · ∇Φ
~ e + σ e Φe − ∂ (SR Φe ) = q ee + q γe + q e ,
Ω
∂E
(2.2)
b ∈ 4π, E > 0,
~r ∈ V, Ω
where Φγ and Φe are the photon and electron angular fluence, respectively, σ γ
and σ e are the macroscopic photon and electron total interaction cross-sections for all
materials in volume V and energies, respectively, q γγ , q γe , q ee represent the photon
scattering source generated from photon interaction, electron scattering source generated from photon interaction, and electron scattering source generated form electron
interactions everywhere in V for all angles and energies, respectively, q γ and q e represent the external photon and electron source from the treatment head, respectively,
b is the unit vector denoting particle
~r is the spatial position vector, E is the energy, Ω
~ is referred to as the streaming operator which may be interpreted
direction, and ∇
as the number of particles flowing out of dV for particles travelling in a direction dΩ
about Ω with energy E about dE.
After solving for the electron angular fluence in Eq. 2.2, the dose in and grid voxel
i can be calculated using
b σ e ~r, E
dΩ
ED
Di =
dE
ρ ~r
0
4π
Z 1
Z
b ,
Φe ~r, E, Ω
(2.3)
D-LBTE like MC uses energy cut-offs for electrons and photons and assumes the
particle is below cut off. AXB cut off for electrons is 500 keV and photons is 10 keV.
Both MC and LBTE have potential sources of error. With MC, errors occur when
an insufficient number of particles have been simulated, whereas LBTE errors are
caused by finite discretization resolution in space, angle and energy. LBTE solvers
use dose-to-water, Dw and dose-to-medium, Dm . The main difference between Dw
and Dm is the post processing step, where Dw is calculated by rescaling Dm using
25
the stopping power ratio of water to medium.
When validating an algorithm it is important to start in a homogenous medium
to find potential errors before testing in more complex mediums. In order to verify
AXB in eclipse, Fogliata et al, used “photon beams of low and high energy in homogenous water with simple geometries. They also included ‘flattening filter free’ (FFF)
beams from the Varian True Beam machine” [12]. With FFF beams the testing was
performed for open fields only with 1% overall accuracy. Testing in open fields in homogenous water to verify accuracy of AXB was compared to measured/golden beam
data, AAA, CCC and MC. In order to verify the output factors, percentage depth
doses (PDD) and lateral doses profiles were evaluated.
D-LBTE has also been tested in heterogeneous mediums including soft tissue,
normal lung, light lung, air, bone, aluminum, stainless steel, and titanium alloy. The
accuracy of D-LBTE is dependent on the material and degree of sampling to the
structural voxels. There have been many investigations in heterogeneity with results
showing considerable difference between AAA and MC. There were little differences
in accuracy when comparing AXB and MC, and AXB had improved dose accuracy
over AAA and CCC.
To verify AXB using IMRT and VMAT humanoid phantoms were used to check
measurements. Humanoid phantoms include Radiological Physics Center (RPC)
phantoms, the anthropomorphic phantom (RANDO phantom, The Phantom Laboratory, Salem, NY), and CIRS thorax phantom (CIRS, VA) [12] Verifications were
performed using thermoluminescent dosimeters (TLDs) with the phantoms and can
be seen in Table 2.1.
The future of dose calculation algorithms is expected to include biological equivalent dose (BED). This can account for biological effect for different types of ionization
beams and different types of irradiated tissues. This allows physicians to compare
the biological dose more closely to the treatment outcome.
26
Table 2.1: A summary of information on some previous experimental validations for the accuracy of D-LBTE solvers in predicting the doses
in heterogeneous humanoid phantoms using multiple clinical setup
fields [12].
The MLC system of a LINAC is extremely complex, both mechanically and
dosimetrically. MLC-involved intensity modulated treatment plans, like IMRT and
VMAT, require quality assurance (QA) on a patient specific basis. Often times these
highly modulated dynamic plans cannot pass the QA criteria. Verification of an
agreement between planned and delivered treatment is crucial for patient care, and
with the emergence of more advanced systems, the need to determine the uncertainties associated with their performance have grown substantially. “Narrow leaf MLC
systems, such as the Varian 120 high-definition (HD) MLC have larger interleaf leakage compared with wide-leaf MLCs. Sometimes, however, an optimal value is also
27
desired, besides the one accurately measured” [13]. The reason the measured value is
not sufficient is due to deviations or parameters that were either not completely modeled or ignored altogether by dose computation algorithm. Additionally, quantities
such as output factors of small fields due to ion chamber volume effects, as well as uncontrollable factors such as target/organ movement during treatment are difficult to
accurately measure. When an optimal value is used, the effect of the aforementioned
uncertainties is minimized [13].
In the Eclipse TPS, for LINACS with rounded MLC leaf ends, Varian recommends
the measurement and input of the following two MLC parameters during commissioning:
• transmission factor - accounts for leakage between leaves
• dosimetric leaf gap (DLG) - related to the gap between light and radiation fields
Using the measured value of the DLG results in failure rates during patient specific
QA, thus tuning of the DLG is recommended in order to improve agreement. The
non-typical, dynamic plans are the best reference for tuning and optimizing the DLG,
where the optimal value is defined as “suitable for as many plans as possible delivered
by a certain LINAC” [13].
The small, irregular shaped fields common in IMRT and VMAT are one of the
biggest dosimetric challenges in radiation therapy. Lateral electron non-equilibrium
and penumbra, as well as tongue and groove effects can significantly change dose
distributions. Lateral penumbra relies on the DLG while longitudinal penumbra is
dependent on the structure of the MLC tongue and groove. Typical plans have
fixed longitudinal field sizes (around 10 cm), so an optimized DLG does not help
with lateral penumbra. However, IMRT and VMAT plans have segments with small
longitudinal field sizes, thus an optimized DLG not only compensates for dose to
the lateral penumbra but to the longitudinal penumbra as well. “Clearly, of the two
28
dosimetric data inputs in the Eclipse TPS, transmission ratio (factor) affects dose in
the entire jaw-formed field and more uniformly that DLG does, whereas DLG mainly
affects the dose from photons passing through the apertures” [13].
29
Chapter 3
Methods and Materials
3.1
Verification of Dosimetric Leaf Gap for Eclipse
Although both TPSs had FFF fields modeled and commissioned, a validation
was performed with respect to the dosimetric leaf gap (DLG) for Eclipse. This was
especially important considering the use of highly modulated fields during this study.
3.1.1
DICOM Files
The necessary fields and instructions required to measure the DLG were provided
directly from the factory by Varian [14]. The measurement proceeded as follows:
• Open field - used for detector alignment and warm up.
• Transm X - fields used to measure transmission for bank A and B. The field is
blocked by the MLC leaves. Abutting rounded leaf edges are positioned under
the collimator jaws. The same collimator jaw settings used as for open field and
fields with sliding MLC gap.
• XXmm - fields with sliding MLC gap. 2, 4, 6, 10, 14, 16, 20 mm gap sizes are
provided. The gap moves from -60 mm to +60 mm with constant speed with
30
respect to MU. The leaves position is defined every 10 mm by a control point.
The resulting fluence is uniform within the field size of 10 x 10 cm2 .
3.1.2
Measurement
The following equipment was used to perform the measurements for the DLG:
• WPD1 water phantom from IBA Dosimetry
• CNMC Instruments Inc Model 206 Dosimetry Electrometer
• PTW Model 30013 waterproof ion chamber
• CNMC Model DBT-100T Digital Barometer/Thermometer
Figure 3-1: WPD1 water phantom from IBA Dosimetry.
31
Figure 3-3: PTW 30013
Figure 3-4: DBT-100T
Figure 3-2: Model 206
The water tank, shown in Figure 3-1, was filled with water and set up with the
surface of the water at 100 SSD. The PTW ion chamber was set at a depth of 10 cm
and the Varian DLG fields listed above were delivered. The Model 206 was used to
take readings in nano Coulombs (nC). First an average transmission reading RT was
calculated using:
RT =
RT,A + RT,B
2
!
(3.1)
where RT,A and RT,B are the transmission reading for MLC Bank A and B, respectively.
Next measured the reading for moving gap Rg using the 1 mm to 20 mm moving
gap fields provided.
32
3.1.3
Calculate DLG
Calculated the contribution of the average MLC leaf transmission to the gap
reading (RgT ) for each gap g as defined by:
RgT = RT ·
g[mm]
1−
120[mm]
!
(3.2)
Note that 120 mm is the leaf travel distance used in the provided DICOM files.
Calculated the corrected gap reading Rg ‘ for each gap g as defined by:
Rg ‘ = Rg − RgT
(3.3)
g(Rg ‘) = aRg ‘ + b
(3.4)
Finally fit the linear function:
For a gap size g of 0mm, a represents the DLG.
3.2
Plan Selection and IMRT/VMAT Creation
Treatment plans were selected from the Phillips Pinnacle TPS for spinal cord
lesions ranging from T10 up to C1, with prescriptions of 30 Gy over five fractions.
Five plans were ultimately chosen.
The process started in Pinnacle with patient 1 and a 6FFF 9-field IMRT plan
to treat the T-spine. The plan was copied, the energy was changed to 10FFF and
it was re-optimized with identical inverse planning objectives. Next the static fields
were swapped for a pair of 360◦ arcs and VMAT plans were optimized for both 6FFF
and 10FFF energies. All plans were computed using a 2 mm dose grid. The process
was repeated for patients 2, 3, 4, and 5, resulting in a total of twenty plans. Each
33
plan was copied to the ArcCheck phantom and a quality assurance (QA) plan was
generated, along with expected point dose per field. The expected fluence and point
doses were then compared to the fields delivered to the ArcCheck. The plans were
then exported into ARIA one by one, and verification plans were created in Eclipse.
The monitor units were verified and dose was computed using a 2 mm dose grid. QA
plans were also generated in Eclipse, as well as expected point dose per field.
Additionally, a dose volume histogram (DVH) was generated for each plan in both
TPSs. The DVHs were exported to the medical imaging management (MIM) system
in order to be compared to each other. Maximum and mean dose as well as volume
coverage of the high dose target, low dose target, and spinal cord were compared
between both the Pinnacle and Eclipse TPSs:
3.3
Dose Verification
Once the plans are in ARIA and planning approved, they can be delivered to the
ArcCheck using the Edge LINAC. Both IMRT and VMAT fields will be delivered
to the ArchCheck patient specific quality assurance (PSQA) device, A 4D isotropic
cylindrical detector array for rotational delivery QA and dosimetry. IMRT plans are
typically QA’d using Sun Nuclear’s MapCheck, a 2D PSQA device, however in order
to minimize setup errors all plans were delivered using one device with one initial
setup.
Some key features of the ArcCheck phantom:
• High sensitivity SunPoint diode detectors (0.019 mm3 )
• Patient Dose and DVH analysis
• High sensitivity SunPoint Diode Detectors
• Measure composite dose and control point ranges
34
• Optional detector inserts for cavity
• Measures flatness/symmetry
• Dose reproducibility
• Measures both entrance and exit dose
The array itself has diameter and length of 21 cm and contains 1386 diode detectors 0.8 mm x 0.8 mm. It is calibrated by the user and operates using a single
power/data cable. Point dose measurements will be performed using an Exradin A16
micro Ion Chamber. The A16, capable of measuring small fields, performs well in
IMRT and VMAT applications where exceptional spatial resolution is necessary.
The ArcCheck was aligned to the wall lasers as shown in Figure 3-5. A final
alignment was performed using the EDGE field light at gantry angles of 0◦ , 90◦ , and
270◦ .
Figure 3-5: The ArcCheck being lined up to the wall lasers. The lasers align
the phantom vertically, laterally, and longitudinally.
The A16 ion chamber was carefully placed inside the ArcCheck cavity as shown
35
in Figure 3-6, and was used to make point dose measurements that were compared
to predicted dose values generated by the planning systems.
Figure 3-6: The A16 as it is inserted into the cavity plug at the front side of
the phantom.
Close the vault door and open the SNC Patient software. It will automatically
recognize the ArcCheck phantom, and immediately take a background reading. Once
the background is complete, select the 6FFF dose calibration and deliver all plans
of that energy. Be sure and save each field after delivery. When complete, select
the 10FFF dose calibration and deliver the remaining fields. Analyze the fields by
comparing them to the QA fields generated by the treatment planning systems. The
criteria used for QA is that the delivered dose distribution measured with a diode
array to be in agreement with the TPS for over 95% of points in the gamma maps
calculated at 3% and 3 mm. Figure 3-7 shows example comparison performed using
the SNC Patient software.
36
Figure 3-7: Field analysis of an VMAT plan, including fluence maps (top)
and diode agreement (bottom).
3.3.1
Optimization of DLG
After the initial data was collected and analyzed using the measured value of the
DLG, an optimization was performed. The DLG parameter was adjusted by 10% until
good agreement between the calculated and measured dose was observed. The value
was then adjusted by a factor of 5% until a better agreement was found. Once the
optimal DLG was determined for both energies, all Eclipse plans and QA fields were
recalculated using the new DLG, and dose verification (Section 3.3) was repeated.
Another comparison of DVHs was also performed.
37
Chapter 4
Results/Discussion
4.0.2
Dosimetric Leaf Gap
Measurements and analysis for the DLG can be seen in Tables 4.1 through 4.3.
Figure 4-1 shows the linear fit used to calculate the DLG, with Table 4.4 showing the
final measured and tuned values used in the Eclipse TPS.
Table 4.1: Transmission from banks A/B and resultant transmission factors.
Table 4.2: Readings for the moving gap.
38
Table 4.3: Contribution of average MLC leaf transmission to gap reading and
the corrected gap reading.
Figure 4-1: Measurements of the dosimetric leaf gap for each energy as a
function of gap size. Linear fits were made and extrapolated to
zero. The value of the reading corresponding to the zero gap size
is the DLG.
39
Table 4.4: The measured values of the DLG for the Varian Edge.
Figure 4-2: Modulation factor vs. % difference in dose with a linear fit.
Eclipse clearly shows an increase in % difference as modulation
factor increases. The strong correlation is supported by a regression analysis
40
Table 4.5: Opmitized values of the DLG for 6FFF and 10FFF.
4.0.3
DVH Comparisons
A dose volume histogram (DVH) was generated in both Pinnacle and Eclipse for
each plan, then transferred to MIM for a comparison of max and mean dose for high
dose targets, low dose targets, and spinal canal. MIM was used as a common platform
in order to achieve the same scale and be able to both visualize and quantify the data.
Target coverage was also analyzed, as was the volume of the spinal cord receiving 14.5
Gy, which is shown in Table 4.6.
Table 4.6: DVH statistics for measured and optimized Eclipse DLG values.
Optimized DLG
Measured DLG
41
Figure 4-3: DVH of 10FFF IMRT for measured DLG.
Figure 4-4: DVH of 10FFF IMRT for optimized DLG.
42
Figure 4-5: DVH of 10FFF IMRT for measured DLG.
Figure 4-6: DVH of 10FFF IMRT for optimized DLG.
43
Figure 4-7: DVH of 6FFF VMAT for measured DLG.
Figure 4-8: DVH of 6FFF VMAT for optimized DLG.
44
Figure 4-9: DVH of 10FFF VMAT for measured DLG.
Figure 4-10: DVH of 10FFF VMAT for optimized DLG.
45
A visual representation of Patient 1 DVH comparisons for the measured and optimized DLG for both energies and techniques can be seen in Figures 4-3 through 4-10.
The Solid line represents Pinnacle statistics while the dashed line represents Eclipse.
4.0.4
Point Dose Measurements
The doses predicted by both the Pinnacle and Eclipse TPSs were compared to
point doses delivered by the Varian Edge to the A16 Ion Chamber during PSQA. Table
4.7 shows the comparison between the measured point doses and values predicted by
the planning systems, with Eclipse’s results representing the measured DLG. Across
all energies, Pinnacle consistently overestimated the dose while Eclipse consistently
underestimated it, which can easily be seen in Figures 4-11 and 4-12.
Table 4.7: Point dose comparisons between the predicted values of Pinnacle
and Eclipse vs. the measured values as delivered by the Varian
Edge prior to optimization of the DLG.
46
Figure 4-11: Predicted dose values generated by Pinnacle compared to actual
delivered dose. Pinnacle had good agreement but tended to
overestimate the dose in most cases.
Figure 4-12: Predicted dose values (measured DLG) generated by Eclipse
compared to the actual delivered dose. Note that Eclipse consistently underestimated the dose across all energies.
47
Table 4.8: Point dose comparisons between the predicted values of Pinnacle
and Eclipse vs. the measured values as delivered by the Varian
Edge after optimizing the DLG.
Figure 4-13: Predicted dose values of Eclipse after the DLG was optimized.
Note that the agreement is much better and matches more
closely than Pinnacle in some cases.
48
Figure 4-14: Isodose comparison between Pinnacle (top) and Eclipse (bottom) as calculated with the optimized DLG. The plan is a 6FFF
IMRT treatment for patient 1.
49
Figure 4-15: Isodose comparison between Pinnacle (top) and Eclipse (bottom) as calculated with the optimized DLG. The plan is a
10FFF VMAT treatment for patient 4.
50
4.0.5
ArcCheck Verification
Table 4.9: The comparison performed with SNC Patient using distance to
agreement (DTA) of 3% and 3 mm with a threshold of 10%. The
Eclipse values for both measured and optimized DLG are listed.
A complete list of individual fields and their corresponding DTA
can be seen in Appendix A
51
Chapter 5
Conclusion
A comparison between the Pinnacle and Eclipse TPSs was performed to evaluate the performance of the modeled FFF fields in IMRT and VMAT plans. Special
consideration was given to the DLG parameter used in the Eclipse TPS, and comparisons were made using both measured and optimized values. When looking at the two
systems individually, Pinnacle’s results were well within tolerances expected from a
clinical setup. Aside from a few fields that involved heavy modulation, all calculated
point doses differed from measured values by less than 5% with an average deviation
of 2.79 ± 2.00%. Gamma analysis with a 3% at 3mm distance to agreement (DTA)
had 99 of 110 fields passing at > 95%. Eclipse on the other hand did not perform well
at first. The measured DLG parameter that was used resulted in calculated point
doses that were more than 8% off from measured values with an average deviation
of -4.44 ± 1.97%, and DTA analysis had a mere 33 of 110 fields passing at > 95%.
However, after optimizing the DLG in Eclipse the results were greatly improved, with
calculated point doses differing from measured values by a maximum of 5.67% with
an average deviation of 2.20 ± 2.23%. A DTA analysis with the updated DLG had 95
of 110 fields passing at > 95%, in which only VMAT fields fell below the established
criteria mark.
While both systems performed well in QA situations where doses were calculated
52
and measured in homogeneous media, there was some discrepancy between the calculated doses under inhomogeneous conditions. The differences were apparent when
comparing the DVHs generated by each TPS. Patients 4 and 5 showed the biggest difference, although not surprising since these two treatment plans were the most highly
modulated and really wrapped the dose around the spinal cord in order to spare any
critical dose to it. While not the aim of this study, a look into comparing the two
systems using an anthropomorphic phantom may offer insight into which performs
better in inhomogeneities.
The concept of the DLG turned out to be a critical component; probably the most
critical. Early analysis showed inaccuracies in the Eclipse TPS that were unacceptable
and would have rendered the study as pointless. However after investigating the DLG
and it’s parameterization for use with IMRT/VMAT plans, an optimal value was
determined and provided results that were in good agreement with measurements.
With respect to both TPSs, IMRT plans from Eclipse showed the best calculated vs.
measured point dose agreement, with an average deviation of 0.22 ± 0.5%. IMRT
plans alone from Pinnacle had a calculated vs. measured point dose agreement of 1.44
± 0.96%. VMAT plans from Pinnacle and Eclipse had values of 4.13 ± 1.88% and
4.19 ± 1.23%, respectively. It is likely that the effect of the DLG on the penumbral
regions of the dose distribution is exacerbated under rotational gantry conditions as
opposed to static step-and-shoot fields.
The Eclipse TPS addresses the rounded MLC by way of the dosimetric leaf gap
(DLG), a parameter that accounts for partial transmission through the rounded leaf
ends of the MLCs. While determining the proper DLG value by measurements is
a fairly straightforward task, it is not sufficient for delivery of highly modulated
treatment fields. VMAT plans, in particular, have shown discrepancies of up to 5%
between calculated and measured doses when using a measured DLG value [15]. The
solution is optimization of the DLG to provide good agreement between the treatment
53
planning systems and measured dose values, however the idea of plan specific DLG
values is being investigated for use in certain situations. Limitations have also been
shown with an optimum or single-value DLG, and studies are looking into the concept
of DLG as a function of the following parameters:
• The distance in the beams eye view between the dose point and the leaf ending
• The width of the MLC
While the factors associated with high-definition MLCs and dynamic treatment
delivery continue to be investigated, it is only a matter of time before their effects
will be fully accounted for by modern dose calculation algorithms. However, as advancements in the field of radiation therapy continue to emerge, there is no doubt
that once this current issue with dynamic delivery is resolved, a new one will almost
certainly present itself.
54
References
[1] Faiz M. Khan, The Physics of Radiation Therapy, 4th ed.
Philadelphia, PA.:
Lippincott Williams and Wilkins, 2010.
[2] Classical
radiation
therapy.
[Online].
Available:
https://medicalphys.files.wordpress.com/2010/12/faiz-khans-02.pdf
(Accessed
April 2016)
[3] H. E. Johns and J. R. Cunningham, The Physics of Radiology, 4th ed. Springfield, IL: Charles C. Thomas, 1983.
[4] Medical Faculty Mannheim. Department of Radiotherapy and Radiation Oncology. Flattening filter free (fff) linac. [Online]. Available: http://www.radiationoncology.de/index.php?page=flattening-filter-free-linac (Accessed March 2016)
[5] NDT Resource Center. X-radiation. [Online]. Available:
https://www.nde-
ed.org/EducationResources/CommunityCollege/Radiography/Physics/xrays.htm
(Accessed April 2016)
[6] Soo M. Chae, Gi W. Lee, and Seok H. Son, “The effect of multileaf collimator
leaf width on the radiosurgery planning for spine lesion treatment in terms of the
modulated techniques and target complexity,” BioMed Central, vol. 9, no. 72,
2014.
[7] Varian Medical Systems, Inc. Multileaf collimators. [Online]. Available:
http://newsroom.varian.com/imagegallery?cat=2473&mode=gallery
55
[8] M. Fippel, New Technologies in Radiation Oncology. Springer Berlin-Heidelberg,
2006.
[9] P. Todd Mcnutt, “Dose calculations: Collapsed cone convolution superposition
and delta pixel beam,” Phillips Medical Systems, Tech. Rep., 2002.
[10] Gregory A. Failla, Todd Wareing, Yves Archambault, and Stephen Thompson,
“Acuros xb advanced dose calculation for the eclipse treatment planning system.”
Varian Medical Systems, Tech. Rep., 2010.
[11] Lanchun Lu, “Dose calculation algorithms in external beam photon radiation
therapy,” International Journal of Cancer Therapy and Oncology, 2013.
[12] Monica W. K. Kan, Peter K. N. Yu, and Lucullus H. T. Leung, “A review on
the use of grid-based boltzmann equation solvers for dose calculation in external
photon beam treatment planning,” BioMed Research International, 2013.
[13] Weiguang Yao, Jonathan B. Farr, “Determining the optimal dosimetric leaf gap
setting for rounded leaf-end multileaf collimator systems by simple test fields,”
Journal of Applied Clinical Medical Physics, vol. 16, no. 4, 2015.
[14] Dosimetric Leaf Gap Measurement, Varian Medical Systems, Inc., 3100 Hansen
Way, Bldg. 4A, Palo Alto, CA 94304-1030, U.S.A., 2010.
[15] Stanislaw Szpala, Fred Cao, and Kirpal Kohli, “On using the dosimetric leaf
gap to model the rounded leaf ends in vmat/rapidarc plans,” Journal of Applied
Clinical Medical Physics, vol. 15, no. 2, 2014.
56
Appendix A
3% at 3 mm DTA Results
57
58
59
60
Appendix B
DVH Statistics
61
62