AAPM TG157
An Overview of Source Modeling and Beam
Commissioning for Clinical Monte Carlo
C-M Charlie Ma, Ph.D.
Department of Radiation Oncology
Fox Chase Cancer Center
Philadelphia, PA 19111, USA
Learning Objectives
• Understand the basics of Monte Carlo radiation
transport and Applications in RT
• Understand the methods for Monte Carlo source
modeling and beam commissioning
• Understand the goals and scope of the AAPM
Task Group 157
Source modeling and beam commissioning for
Monte Carlo dose calculation based radiation
therapy treatment planning
C Ma, I Chetty, J Deng, B Faddegon, SB Jiang, JS Li, J Seuntjens, J Siebers, E Traneus
The AAPM Task Group 157 Report
1
What is Monte Carlo ?
(a computer simulation technique using random sampling)
John Von Neumann
(1903-1957)
Stanislow M. Ulam
(1909-1984)
After the city in the Monaco principality ...
What is Monte Carlo
Radiation Transport?
• Random sampling of particle interactions
a good supply of random numbers
probability distributions governing the physics processes
• Information obtained by simulating large
number of histories
Photons
e-
e+
2
Simulation of Clinical Accelerators
Tissue Type/Mass Density Determination
(Pawlicki 1998)
0.05
512 x 512
Frequency
0.04
128 x 128
64 x 64
0.03
0.02
0.01
0.00
0
200
400
600
800
1000
1200
1400
CT Number
CT Number to Medium Conversion
2.5
125~1.101g/cm3
-700~0.302g/cm3
-950~0.044g/cm3
-1000~0.001g/cm3
Mass density (g/cm^3)
teflon
2000~2.088g/cm3
2.0
1.5
Skeleton - Ribs
polycarbonate
Vertebral body
water
polyethylene Alderson Muscle
1.0
0.5
Alderson Lung
air
0.0
0
500
1000
1500
2000
2500
CT Number
3
Density map
Medium map
Applications of M-C in radiotherapy
•
•
•
•
•
•
Fluence and spectrum calculations
Dosimetric parameters (stopping powers, etc.)
Correction factors (BSF, HS, PS, P/S ratio...)
Dosimeter response simulations
Treatment head simulations
Treatment planning dose calculations
Comparisons of Pencil Beam and Monte Carlo
Mohan et al (1997)
Conventional Method (Pencil Beam)
PEREGRINE
70 Gy
70 Gy
Tumor
4
Dose Verification for IMRT
(Pawlicki et al 2000)
Monte Carlo
Corvus
Gy
16.2
14.4
12.6
9.0
5.4
1.8
DVH Comparison
100
MC
Corvus
Volume (%)
80
60
GTV
RT Lung
40
20
Cord
0
0
5
10
15
20
Dose (Gy)
CyberKnife Lung SBRT
(Ma et al 2009)
5
MU real patient vs.water tank
(MC / Water tank= 292 / 256=1.14)
Courtesy of Joanna Cygler
MU real patient vs.water tank
Target 1,2 MC
based MU
Target 1,2 water
tank based MU
Lt eye water
tank based
Lt eye MC
MU
based MU
Rt eye water
tank based
MU
Rt eye MC
based MU
Internal mammary nodes
(MC / Water tank= 210 / 206=1.019)
Courtesy of Joanna Cygler
6
Implementation of MCTP
• Accelerator simulation
• Source modeling
• Beam commissioning
AAPM TG-157
• CT data conversion and phantom setup
• Dose calculation algorithms
• Data processing and statistical analysis
AAPM TG-105 MC dose calculation for RTP
(Chetty et al Med Phys (2007) 34: 4818-53)
Radiotherapy Treatment Planning
Beam data
Prescription
Patient CT scan
Plan description
Dose calculation
Display
Plan evaluation
Creation of Beam Phase Space for MC
Measured data
Commissioning
Four routes:
1
2
3
4
Beam model
Modeling
Sampling
Linac simulation
Phase space
7
Goals of AAPM TG-157
• Review different beam model implementations
• Provide guidelines on types of tests needed for
acceptance testing and commissioning of MCbased beam models
• Provide guidelines for tolerance criteria for
acceptance testing and commissioning of beam
models to be used for MC-based clinical dose
calculation
8