Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
This document is the handout for the Monte Carlo
Refresher Course presented 44th Annual AAPM
Meeting in Montreal, Canada.
Monte Carlo for Radiation
Therapy Dose Calculations
Since the AAPM restricts the file size of the hand
out to be 1 MB, this hand out does NOT include
all slides presented at the meeting. Instead, this
hand-out covers major points. A link to the full
presentation is available at
http://www.radonc.rdo.vcu.edu/AAPM
MC Refresher Course
44th Annual AAPM Meeting
Montreal, Canada
Jeffrey V. Siebers
Virginia Commonwealth University
Medical College of Virginia Hospitals
Richmond, Virginia USA
Educational Objectives
Outline
To understand MC
method
n commissioning
n statistical noise
n comparison methods
n potential clinical significance
n
Historical review of MC method
n Basics of MC transport
n Description of an MC system for
patient dose calculations
n Commissioning MC algorithms
n
Ø
Ø
Determination of initial phase space
Dose normalization
Outline
n
Patient calculations
Ø
Ø
Ø
Ø
Ø
Converting CT data to patient materials
MC dose grid / CT dose grid differences
Dose to material / dose to water
Effect of statistical noise
Methods to reduce statistical noise
Outline
n
MC treatment planning
Ø
Comparing MC with SC and PB for
n
n
Ø
n
3DCRT
IMRT
Role of MC in IMRT optimization
MC as a tool for IMRT dosimetric
verification
1
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Historical Review of
Monte Carlo
Historical Review
Compiler Development
Early Beginnings
1772: Compte de Buffon
n
Ø
Uses random sampling to solve mathematical
problem
n
Ø
1945: ENIAC
n
Ø
First large scale electronic computer (John
Mauchly)
1945: Stan Ulam, John van Neumann,
Nicholas Metropolis
n
Ø
Ø
1954-1957: Fortran
Ø
n
IBM (John Backus)
First successful high level language
1961: Fortran IV
Ø
Standardized Fortran
Propose using “computers” to solve neutron
diffusion problems
Coined name “Monte Carlo” (Metropolis)
Historical Review
Historical Review
Early “general purpose” codes
n
1963: MCS
Ø
n
1964: ETRAN (Martin Berger)
n
1962: O5R
Ø
Ø
n
n
Precursor to MCNP, general purpose MC code
Ø
Ø
Ø
Condensed history approach
Predecessor of NTC, NMTC, HETC, LAHET,
MCNP-X intranuclear cascade codes
1974: EGS1 (Ford and Nelson)
EGS3 being used for Med Physics
n
1983: Petti, contaminant electron studies
1984: Rogers & Bielajew
1985: Mohan, energy spectra
1985: EGS4
Ø
Ø
1986: Rogers and Bielajew publish first Med
Phys papers on EGS4 (Med Phys, 13 5)
256 References in PubMed for EGS4 (6/02)
Historical Review
n
1993: Peregrine Project formed at LLNL
n
1995: BEAM and DOSXYZ
Ø
Ø
Ø
n
What is Monte Carlo?
BEAM: Rogers et al, Med. Phys. 22 5
DOSXYZ: Ma et al PIRS-0509b, NRCC, 1995
Other Therapy Specific MC codes
Ø
Ø
n
Radiation Therapy Specific MC code
1996: VMC/XVMC/VMC++ (Kawrakow et al)
2000: DPM (Sempau et al)
795 “hits” in PubMed with Monte Carlo
+ radiation + therapy
2
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
What is Monte Carlo?
What is Monte Carlo?
Simple Example
Simple Example
Ø
Given, photon of Energy E incident on
infinite (water) phantom
n
n
Determine interaction probabilities
Determine which interaction occurred
by selecting another random number
(RN’)
∑total =∑PhotoEffect
PhotoEffect +∑Compton +∑Pair
n
Select Random Number (RN (0,1]) to choose
interaction distance
Ø
Photo Effect occurs if
Ø
Compton Effect occurs otherwise
RN ' < ∑ PhotoEffect ∑total
n
x = − ln ( RN ) ∑ total (cm)
Determine interaction products by
sampling further distributions
Ø
Energy and angle (direction) of scattered
photon / electron
What is Monte Carlo?
Monte Carlo Method
Simple Example
n
Score quantities of interest
Ø
Ø
n
n
Energy Deposition (Dose)
Fluence
Follow particles (and secondaries )
until they are no longer of interest
n
Particle escapes geometry
Particle is absorbed
Particle drops below energy cut-off
n
Ø
Ø
Ø
Follows the path of individual
representative particles through
accelerator, beam modifiers, and patient
to determine dose, fluence, and other
distributions in patients and phantoms
Uses basic physics interaction
probabilities (sampled via selection of
random numbers) to determine the fate of
the representative particles
Sufficient representative particles are
transported to produce a statistically
acceptable results (averages)
Monte Carlo
Monte Carlo Method
Items of Interest
The particles transported only represent
real particles
n Only ~100 Million particles will be used in
a patient simulation
n During a 2 Gy fraction
~1016 electrons incident upon the target,
~1014 photons impinging on the patient
n Increasing number of particles
transported increases computer time
(linearly) but only improves statistics by
the square root of the number of particles
Target Backing
Target
e-
Primary
Collimator
Bremsstrahlung
n
Flattening
Filter
Ion Chamber
Compton
Jaws
eBremsstrahlung
Compton
MLC
CSDA
Annihilation
e+
ePair
e-
Patient
EPID
3
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
MC Program Flow
Why bother with Monte
Carlo?
Sample next
source particle
Yes
Stack empty
Terminate history
Put particle on top of
last-in first-out stack
No
n
Current algorithms are accurate
enough
n
Clinical experience is with current
inaccurate algorithms
n
Monte Carlo takes too long
Select particle from
top of stack
No
Energy > cutoff &
particle in geometry
Yes
Electron
Electron or photon?
Process electron
transport (creates 2nd aries)
Photon
Process photon
transport (creates 2nd aries)
Record events of interest
(energy deposition, fluence …)
Why Monte Carlo?
n
Radiation transport is a complex
process
Ø
Ø
Why Monte Carlo?
n
Accuracy of currently available dose
computation models for planning of
radiation treatments is limited
n
Discrepancies compared to true dose
distributions may be clinically significant
for many cases
Electron interactions result in
n
Photons (Bremsstrahlung + characteristic x-rays)
n
Delta-rays (knock on electrons)
Photon interactions result in
n
Photons (Compton, Pair Production …)
n
Secondary electrons (Compton, Photoelectrons)
Current methods might have errors!
Why Monte Carlo?
Why Monte Carlo?
n
The discrepancies revealed by accurate
predictions of dose can be remedied using
different treatment techniques, e.g., use of
different margins, beam energies, beam
arrangements, and intensity modulation
n
n
n
n
High accuracy is now practical and
affordable with Monte Carlo simulations of
radiation transport
We can do something about it!
n
Universal accuracy: all materials,
modalities, anatomic geometries, devices, ...
Can simulate ACTUAL beam delivery
(moving MLC’s , dynamic wedges, etc).
Elimination of laborious trial and error
parameterization and refinement of models
Reduction in time and the amount of
measured dose distribution data required
for commissioning and validation
It might even be easier!
4
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Why Monte Carlo?
n
n
n
n
Direct prediction of monitor units reducing
the probability of human mistakes
Improvement in consistency of interinstitutional results
Improvement in quality of dose response
data
Accurate estimation of quantities difficult
or impossible to measure
How do we do Monte Carlo
dose calculations?
Accurate dose has benefits!
Target
Collimator
Vacuum Win
Flattening Filter
Ion Chamber
PSD Plane
Stage 1: PSD Generation
Transport particles to IC exit
Stage 1: Creation of Initial
Phase Space
Jaws
MLC
Stage 2: Patient Calculations
Transport particles through
patient dependent devices.
(jaws, blocks, mlc, wedges,
and patient/phantom)
Blocks
Wedges
Patient / Phantom
n
Method
n
Sensitivity to incident
electron beam parameters
n
Verification and validation
Initial Phase Space
(Ψ(E,x,y,u,v) )
Input Accelerator Geometry
MCNP Geometry plotted with Sabrina
BEAM Geometry plotted with EGS-Windows
n
Assume
Ø
Ø
Ø
n
n
Electron beam is radially symmetric and Gaussian
Geometry specification is correct
…
Iterate adjusting E, s E, s R to match
profiles and depth dose
Recent papers on this…
Ø
Ø
D. Sheikh-Bagheri and D. W. Rogers, “Sensitivity of megavoltage photon beam
Monte Carlo simulations to electron beam and other parameters,” Med Phys 29
(3), 379 -90 (2002).
G. X. Ding, “Energy spectra, angular spread, fluence profiles an d dose
distributions of 6 and 18 MV photon beams: results of Monte Carl o simulations
for a Varian 2100EX accelerator,” Phys Med Biol 47, 1025-46 (2002).
5
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Initial Phase Space
Initial Phase Space
Dependence of Depth Dose on Energy
Dependence of lateral profile on energy
1.8
1.2
1.8
1.0
Measurement
Monte Carlo E = 17.0 MeV
Monte Carlo E = 19.0 MeV
1.6
0.8
0.4
0.2
0
10
20
Depth (cm)
30
Relative dose
1.4
0.6
1.00
1.02
0.98
Measurement
Monte Carlo E = 5.6 MeV
Monte Carlo E = 6.4 MeV
1.2
1.0
0.96
40
5
0.8
10
X (cm)
Relative dose
Relative dose
1.4
Relative dose
1.02
Measurement
Monte Carlo E = 5.6 MeV
Monte Carlo E = 6.4 MeV
1.6
1.00
15
0.98
Measurement
Monte Carlo E = 17.0 MeV
Monte Carlo E = 19.0 MeV
0.6
0.4
0.96
0.2
0
10
20
30
40
5
Monte Carlo dose per
particle to dose per MU
10
15
X (cm)
Depth (cm)
Target
Collimator
Vacuum Win
Flattening Filter
Ion Chamber
PSD Plane
Save Initial Phase
Space for Future Use
Jaws
n
n
Normalize to a point
or
Integrate measured and MC 10×10 inphantom depth dose curves between
5 and 15 cm
n
Ø
Blocks
Wedges
K = ∫ 5 Dmeasured ( z) dz ∫ 5 Dcomputed ( z )dz
Dose 
 Fluence MU = Dose


MU Fluence 

15
n
15
n
Single MU calibration factor used for
all fields
n
n
n
n
n
A. E. Schach von Wittenau, L. J. Cox, P. M. Bergstrom, Jr., W. P. Chandler, C. L.
Hartmann Siantar, and R. Mohan, “Correlated histogram representation of Monte
Carlo derived medical accelerator photon- output phase space,” Med Phys 2 6 (7),
1196-211 (1999)
J. V. Siebers, P. J. Keall, B. Libby, and R. Mohan, “Comparison of EGS4 and
MCNP4b Monte Carlo codes for generation of photon phase space distributions
for a Varian 2100C,” Phys Med Biol 44 (12), 3009- 26 (1999)
J. Deng, S. B. Jiang, A. Kapur, J. Li, T. Pawlicki, and C. M. Ma, “Photon beam
characterization and modelling for Monte Carlo treatment planning,” Phys Med
Biol 45 (2), 411- 27 (2000)
I. Chetty, J. J. DeMarco, and T. D. Solberg, “A virtual source model for Monte
Carlo modeling of arbitrary intensity distributions,” Med Phys 2 7 (1), 166-72
(2000)
M. K. Fix, H. Keller, P. Ruegsegger, and E. J. Born, “Simple beam models for
Monte Carlo photon beam dose calculations in radiotherapy,” Med Phys 27 (12),
2739-47 (2000)
M. K. Fix, M. Stampanoni, P. Manser, E. J. Born, R. Mini, and P. Ruegsegger, “A
multiple source model for 6 MV photon beam dose calculations using Monte
Carlo,” Phys Med Biol 4 6 (5), 1407 -27 (2001)
Phase space particles
from BEAM
simulations of
upstream beam line
Phase Space
Models
Patient / Phantom
Commissioning /
Acceptance testing
Phase Space References
n
Phase Space Files
MLC
n
n
n
Set acceptance criteria for dose profile
(2%, 2mm) and output agreement (1%)
Water phantom comparisons
Ø
Depth Dose (open and wedged, various field sizes)
Ø
Lateral Profiles (open and wedged, various field
sizes)
Dose profile comparisons in specific
materials / interfaces
6
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Commissioning /
Acceptance testing
n
Standard Tx planning tests (TG-53)
Ø
Ø
Orientation, device selection, …
Calculation verification
CT number to material conversion
n Users will likely perform additional
confidence building tests
n
Dosimetric
Verification of
a PSD
(LLNL
Peregrine)
Dosimetric Verification
References
n
n
n
n
n
n
C. L. Hartmann Siantar, R. S. Walling, T. P. Daly, B. Faddegon, N. Albright, P.
Bergstrom, A. F. Bielajew, C. Chuang, D. Garrett, R. K. House, D. Knapp, D. J.
Wieczorek, and L. J. Verhey, “Description and dosimetric verification of the
PEREGRINE Monte Carlo dose calculation system for photon beams incident on
a water phantom,” 2 8 (7), 1322-37 (2001).
C. M. Ma, E. Mok, A. Kapur, T. Pawlicki, D. Findley, S. Brain, K. Korster, and A.
L. Boyer, “Clinical implementation of a Monte Carlo treatment pl anning system,”
Med Phys 26 (10), 2133 -43 (1999)
E. Spezi, D. G. Lewis, and C. W. Smith, “Monte Carlo simulation and dosimetric
verification of radiotherapy beam modifiers,” Phys Med Biol 46 (11), 3007- 29
(2001)
L. Wang, M. Lovelock, and C. S. Chui, “Experimental verification of a CT -based
Monte Carlo dose-calculation method in heterogeneous phantoms,” Med Phys 2 6
(12), 2626- 34 (1999)
M. Fippel, W. Laub, B. Huber, and F. Nusslin, “Experimental investigation of a
fast Monte Carlo photon beam dose calculation algorithm,” Phys Me d Biol 44
(12), 3039- 54 (1999)
J. S. Li, T. Pawlicki, J. Deng, S. B. Jiang, E. Mok, and C. M. Ma, “Validation of a
Monte Carlo dose calculation tool for radiotherapy treatment pla nning,” Phys Med
Biol 45 (10), 2969- 85 (2000)
CT to Material
Conversion
n
Conversion of patient CT image
for MC transport
n The MC run
n Effect of patient noise
n Dose to water conversion
n Plan comparisons
n
ctcreate blending of voxels
ctcreate (BEAM distribution)
Ø
Ø
n
Stage 2: Patient Simulation
uses mean CT number in dose grid voxel to
assign density and material
uses dose grid voxels for particle transport
52 materials in CT-to-density
conversion
Ø
Ø
Ø
covers density from 0-2.0 g/cm2
most materials from ICRU-46
to minimize error in dose-to-material
conversion
7
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Patient Simulations
Voxel Blending
An example of MC integration
into commercial TPS
n Effect of MC Noise
n Dose to ?
n Plan Comparisons
n
Reduces resolution
n Homogenizes patient
n May impact dose at interfaces
n
n
Note: CT data itself is
homogenization…
Example Monte
Carlo Code
Implementation
Ø
Ø
Ø
Ø
Ø
Ø
MCV developed interface to NRCC EGS4
BEAM / DOSXYZ code
BEAM used for transport through treatment
head (Jaws, wedges, etc)
Internal MC routines used for MLC and EPID
simulations
DOSXYZ for patient / phantom simulation
Interfaced to Pinnacle treatment planning
system
Unix workstations (multi -processor,multi computer)
Breast Case Comparison
4 field
Pinnacle
MCV
Dose/FX (cGy)
Breast
Dose Difference: MCV-Pinnacle
MCV-Pinnacle
Effect of Statistical Noise
n
n
Each dose point has statistical
uncertainty
Effect on plan evaluation
Ø
Ø
Ø
Dose Difference (cGy)
n
n
Isodose
DVH
TCP / NTCP / EUD
Effect on prescription
Methods to reduce noise
8
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Effect of Statistical Noise
Patient Prescriptions
As the number of points in a dose distribution increases,
so does the maximum deviation from the mean
Consequence
Unacceptable: Point Dose Prescriptions
n
Acceptable level (~2%)
Ø
n
Removing from DVH
Ø
Prescribe 200 cGy per fraction to 90% of maximum dose
Acceptable: Regional or Dose (MU) based prescriptions
Prescribe 200 cGy per fraction to 98% of the tumor volume
Methods to reduce statistical
noise
n
P. J. Keall, J. V. Siebers, R. Jeraj, and R. Mohan, “The effect of
dose calculation uncertainty on the evaluation of radiotherapy
plans,” Med Phys 27 (3), 478-84 (2000).
Ø
J. Sempau and A. F. Bielajew, “Towards the elimination of Monte
Carlo statistical fluctuation from dose volume histograms for
radiotherapy treatment planning,” Phys Med Biol 45 (1), 131-57
(2000).
S. B. Jiang, T. Pawlicki , and C. M. Ma, “Removing the effect of
statistical uncertainty on dose-volume histograms from Monte Carlo
dose calculations,” Phys Med Biol 45 (8), 2151-61 (2000).
Example of denoising…
Denoising / Smoothing
Ø
J. O. Deasy, “Denoising of electron beam Monte Carlo dose
distributions using digital filtering techniques,” Phys Med Biol 45 (7),
1765-79 (2000).
Ø
WE-D-517D-2: Miao et al: “3-D Anisotropic Diffusion and Wavelet
Filtering of Monte Carlo Dose Distribution”
WE-D-517D-4: Kawrakow: “Smoothing Monte Carlo Calculated
Dose Distributions for Radiation Treatment Planning”
Ø
Example of denoising…
Example of denoising…
9
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Example of denoising…
Denoising
Can reduce MC dose calculation
time by factor of ~8
n Can introduce artifacts
n Must be applied carefully
n
(see papers and posters)
How to compare with MC?
Dose to water or dose to
water?
Absorbed Dose to Water
Statement of the problem
n
n
n
n
n
Measurements are typically in terms of Dwater
Current clinical experience in radiation
therapy is based upon Dwater
“Conventional” algorithms compute Dwater
Monte Carlo dose algorithms most accurate
when they compute Dmedium
To compare, need a method to convert
Dwater to Dmedium .
Water-to-Material Stopping
Power Ratios
n
Method of conversion
Ø
n
J. V. Siebers, P. J. Keall, A. E. Nahum, and R. Mohan, “Converting absorbed
dose to medium to absorbed dose to water for Monte Carlo-based photon
beam dose calculations,” Phys Med Biol 4 5 (4), 983- 95 (2000).
AAPM Point / Counterpoint
Ø
Ø
H. H. Liu, “Dm rather than Dw should be used in Monte Carlo treatment
planning. For the proposition,” Med Phys 2 9 (5), 922-3 (2002).
Dm rather than Dw should be used in Monte Carlo treatment planning.
Against the proposition,” Med Phys 29 (5), 923- 4 (2002)
Patient Plan Comparisons
10
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Breast Case
Breast Case
Isodose Comparison
Dose Difference Display
Pinnacle
Dmaterial
Dwater
MCV - Pinnacle
MCV - Pinnacle
MCV
+10
+5
+3
+2
+1
-1
-2
-3
-5
-10
Breast
Dose Difference: MCV-Pinnacle
Dwater
Lung Case
MCV
MCV - Pinnacle
MCV - Pinnacle
MCV-Pinnacle
MCV - Pinnacle
Dose Difference (%)
Dose Difference (%)
Relevant Papers for MC
Comparisons
Head and Neck
Case
n
MCV
MCV - Pinnacle
n
Pinnacle
MCV
n
n
P. Francescon, C. Cavedon, S. Reccanello, and S. Cora, “Photon dose
calculation of a three -dimensional treatment planning system compared to the
Monte Carlo code BEAM,” Med Phys 27 (7), 1579- 87 (2000)
C. M. Ma, E. Mok, A. Kapur, T. Pawlicki, D. Findley, S. Brain, K. Korster, and A.
L. Boyer, “Clinical implementation of a Monte Carlo treatment pl anning system,”
Med Phys 26 (10), 2133 -43 (1999)
M. Miften, M. Wiesmeyer, A. Kapur, and C. M. Ma, “Comparison of RTP dose
distributions in heterogeneous phantoms with the BEAM Monte Carlo simulation
system,” J Appl Clin Med Phys 2 (1), 21- 31 (2001)
L. Wang, E. Yorke, G. Desobry, and C. S. Chui, “Dosimetric advantage of using 6
MV over 15 MV photons in conformal therapy of lung cancer: MonteCarlo
studies in patient geometries,” J Appl Clin Med Phys 3 (1), 51-9 (2002)
Dose Difference (%)
11
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
Head and Neck Case
Monte Carlo and IMRT
MCV - Pinnacle
n
n
n
n
n
Impact for IMRT???
n
n
IMRT
Consequences of inaccuracy
n
Consequences of
inaccuracy
Systematic error
Ø
Ø
For a given intensity distribution, dose
predicted differs from that actually delivered
to the patient/phantom
Can be avoided by performing final
calculation with accurate algorithm
n
Convergence error
Ø
Consequence of systematic error during optimization
Ø
Optimization with an inaccurate algorithm results in
different intensities than those predicted by an
accurate algorithm
Ø
Actual dose is not optimal, a better solution exists
Can be avoided by optimization with an accurate
algorithm
Ø
IMRT
Comparison between Film and SC
on Flat Phantom
(a)
(b)
R. Jeraj and P. J. Keall, “The effect of statistical uncertainty on inverse treatment
planning based on Monte Carlo dose calculation,” Phys Med Biol 4 5 (12), 3601-13.
(2000)
R. Jeraj, P. J. Keall, and J. V. Siebers, “The effect of dose calculation accuracy on
inverse treatment planning,” Phys Med Biol 47 (3), 391-407 (2002)
C. M. Ma, T. Pawlicki, S. B. Jiang, J. S. Li, J. Deng, E. Mok, A. Kapur, L. Xing, L. Ma,
and A. L. Boyer, “Monte Carlo verification of IMRT dose distributions from a
commercial treatment planning optimization system,” Phys Med Biol 45 (9), 2483- 95
(2000)
T. Pawlicki and C. M. Ma, “Monte Carlo simulation for MLC-based intensitymodulated radiotherapy,” Med Dosim 26 (2), 157- 68 (2001)
W. U. Laub, A. Bakai, and F. Nusslin, “Intensity modulated irradiation of a thorax
phantom: comparisons between measurements, Monte Carlo calculations and pencil
beam calculations,” Phys Med Biol 4 6 (6), 1695- 706 (2001)
W. Laub, M. Alber, M. Birkner, and F. Nusslin, “Monte Carlo dose computation for
IMRT optimization,” Phys Med Biol 45 (7), 1741-54 (2000)
J. V. Siebers, M. Lauterbach, S. Tong, Q. Wu, and R. Mohan, “Reducing dose
calculation time for accurate iterative IMRT planning,” Med Phys 29 (2), 231- 7 (2002)
VCU IMRT Case
(c)
12
Monte Carlo for Radiation Therapy Dose Calculations
Monte Carlo Refresher Course
AAPM 2002
Jeffrey V. Siebers, VCU
IMRT
Comparison between Film and SC
on Flat Phantom
(a)
(b)
(c)
Questions to ask your MC
vendor / developer?
n
What is the acceptance criteria (systematic errors)?
n
How fast is the Code (field size, voxel size, Tx volume)?
What is the statistical uncertainty at that quoted speed?
n
n
n
How much $$ must I spend on computers?
Does it compute D water so I can compare results with
other algorithms and relate to my clinical experience?
Points with a dose difference <2% or a DTA <2 mm are considered dosimetrically equivalent. For the MC
computation, 97% of the points fall in that category
Summary
MC History
n Basics of MC
n Commissioning of MC
n Patient Calculations
n MC and IMRT
n
13