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Independent Dose Verification for IMRT Using Monte Carlo. C-M Charlie Ma, Ph.D. Department of Radiation Oncology FCCC, Philadelphia, PA 19111, USA. Outline. Why Monte Carlo for IMRT? Implementation procedure Experimental verification Treatment plan comparison for IMRT
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Independent Dose Verification for IMRT Using Monte Carlo C-M Charlie Ma, Ph.D. Department of Radiation Oncology FCCC, Philadelphia, PA 19111, USA
Outline • Why Monte Carlo for IMRT? • Implementation procedure • Experimental verification • Treatment plan comparison for IMRT • Monte Carlo as an independent QA tool
John Von Neumann (1903-1957) Stanislow M. Ulam (1909-1984) What is Monte Carlo ? • The originators: Von Neumann and Ulam 1949 • The method: Random sampling from pdf’s to construct solutions to problems.
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
A shower of particles in a phantom 9 MeV electrons
Why Use Monte Carlo forRadiotherapy Treatment Planning An accuracy of about 5% in dose delivery is required to effectively treat certain types of cancers and to reduce complications. ICRU Reports 24 (1976) and 42 (1988)
The Accuracy Requirement forTreatment Planning Dose Calculationif s2=s2calib+s2dose+s2setup+s2motion +… and s 2sdose=5% then sdose=2.5%
Why Accurate Dosimetry for IMRT? • Limitations of dose calculation algorithms • source models • narrow beam geometry • heterogeneous patient anatomy Limitations of beam delivery systems • complex intensity distributions • complex MLC geometry
Beamlet Dose Distributions FSPB Monte Carlo 9070505 9070505
Effect of Beam Delivery Systems • Effect of leaf leakage and scatter • leaf size • leaf shape • collimator jaw position Effect of leaf sequence • dynamic vs step-and-shoot • leaf synchronization
NX = 1 NX = 2 NX = 3 LEAF A LEAF B 1 2 3 4 5 6 7 …. N X Z MLC LEAVES SIDE VIEW - ROUNDED ENDS MLC CARRIAGE FRONT VIEW - NON DIVERGENT ENDS
One field intensity map comparison • Sharp • No T&G • Fuzzy • T&G T&G
Fluence and Dose Comparison 8% underdosage for a single field with 1 mm voxels 6% underdosage for 4 mm voxels 2% underdosage averaged over 1 cm leaf width T&G causes underdosage in high dose regions T&G causes overdosage in low dose regions
Monte Carlo dose comparison for one field (Differences seen at locations indicated by arrows) Without tongue-and-groove effect With tongue-and-groove effect
Comparison of IMRT dose distributions (prostate) With (thin lines) and without (thick lines) T&G effect Doses with T&G effect increased by 1.6%
DVH comparison between prostate plans with and without tongue-and-groove effect Lines: without T&G Line-and-symbols: with T&G By shifting doses with T&G horizontally by 1.6%, two plans matches
Fluence Map Extra-focal source MLC plane Fluence map Leakage Isocenter plane
Delivery in film phantom Patient plan (Corvus) 1 cm from isocenter Monte Carlo dose calculation 3 cm from isocenter 2 cm from isocenter superior inferior Film vs Monte Carlo
Calculations vs. Measurements CORVUS Energy Meas M-C 2.177 Gy 2.177 Gy 2.201 Gy 4 MV 2.146 Gy 2.161 Gy 2.276 Gy 15 MV
Monte Carlo for MU calculation/check • high accuracy and high efficiency • independent calculation Monte Carlo for patient dose verification • predict patient dose using leaf sequence • verify patient dose using MLC log files
Corvus Monte Carlo Gy 77.2 70.0 56.1 48.9 35.0 27.8 21.1 13.9 Prostate
Prostate 100 MC 80 Corvus Prostate 60 Volume (%) Bladder 40 20 Rectum 0 0 15 30 45 60 75 90 Dose (Gy)
Effect of Couch Bar on IMRT Dose Distribution Attenuation for 18 MV photons Without bar MC vs film measurement With bar
Effect of Couch Bar on IMRT Dose Distribution IMRT using 6 MV photons IMRT using 18 MV photons
Dose Reconstruction Using MLC Log Files Original map Rebuilt map Error map showing discrepancies between planning and delivery
Acknowledgments The FCCC/Stanford Monte Carlo Team Charlie Ma Bob Price Lili Chen Eugene Fourkal Jinsheng Li Shawn McNeeley Kamen Paskalev Lu Wang Steve Jiang Todd Pawlicki Jun Deng Bilal Shahine Ajay Kapur Michael Lee William Xiong Sotirios Stathakis Jay Chen Wei Luo Antonio Leal James Fan Freek Du Plessis Jie Yang Lihong Qin Meisong Ding Omar Chibani Grisel Mora Thai Bing Nguyen