Monte Carlo Dose Calculation for Radiotherapy Treatment Planning
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1 Monte Carlo Dose Calculation for Radiotherapy Treatment Planning Monte Carlo Dose Calculation for Radiotherapy Treatment Planning C-M M Charlie Ma, Ph.D. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia, PA 19111, USA Chetty I, Curran B, Cygler J, DeMarco J, Ezzell G, Faddegon B, Kawrakow I, Keall P, Liu H, Ma C-M, Sheikh-Baghery D, Rogers D, Seuntjens S, Siebers S. The AAPM Task Group 105 Report Med Phys (2007) 34: What is Monte Carlo? (a mathematical method using random sampling) The originators : Von Neumann and Ulam 1949 The method : Random sampling from pdf s to construct solutions to problems. Why call it Monte Carlo? John Von Neumann ( ) Stanislow M. Ulam ( ) After the city in the Monaco principality... 1
2 What is Monte Carlo Radiation Transport? Photons 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 e- e+ CPU time required 1 Gy => 1 billion electrons 1% uncertainty for 0.3 cm cubes requires a few million electrons (hours on a PC) 1 Gy => 1000 billion photons 1% uncertainty for 0.3 cm cubes requires up to a few billion photons (days on a PC) 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 2
3 Correction factor for bone for 6 MV photons water bone Correction factor for bone Cl2300 C/D 6MV calculated by FOCUS measured with ion chamber calculated by EGS4/DOSXYZ water Comparisons of Pencil Beam and Monte Carlo Mohan et al (1997) Conventional Method (Pencil Beam) PEREGRINE 70 Gy Tumor 70 Gy cm Depth (cm) Dose Verification for IMRT (Pawlicki et al 2000) 100 DVH Comparison 80 MC Corvus GTV Monte Carlo Gy Corvus Volume (%) RT Lung Cord Dose (Gy) 3
4 Implementation of MCTP Simulation of Clinical Accelerators Accelerator simulation Source modeling Beam commissioning CT data conversion and phantom setup Dose calculation algorithms Data processing and plan evaluation Plan optimization Tissue Type/Mass Density Determination Frequency (Pawlicki 1998) 512 x x x CT Number CT Number to Medium Conversion 2000~2.088g/cm3 125~1.101g/cm3-700~0.302g/cm3-950~0.044g/cm3-1000~0.001g/cm3 Mass density (g/cm^3) air CT Number teflon Skeleton - Ribs polycarbonate Vertebral body water polyethylene Alderson Muscle Alderson Lung 4
5 M-C Simulation Geometry Source plane contours Density map Medium map Beam modifier CT phantom Monte Carlo Algorithms Electron beams reported VMC DOSXYZ MCDOSE/MCSIM PENELOPE MCRTP DPM Photon beams reported XVMC PEREGRINE MCNP DOSXYZ MCDOSE/MCSIM/MCRS Current Status of MCTP BrainLab NOMOS Varian Accuray CMS Nucletron AAPM TG157 Survey 5
6 Measured vs MC Reconstructed Dose Distributions Beam Commissioning for MC 6 MV 40cmx40cm 18 MV 40cmx40cm Yang et al, Phys Med Biol (2004) 49: Combined Dose Distribution: Film vs Monte Carlo Patient plan (Corvus) Delivery in film phantom Monte Carlo dose calculation Calculations vs. Measurements superior inferior cm from isocenter Energy M-C Meas CORVUS 4 MV Gy Gy Gy 15 MV Gy Gy Gy Lee et al (2001) 6
7 Treatment Plan Comparison Prostate Treatment Plan Comparison Monte Carlo Gy Corvus Prostate T11-L1 Vertebra Volume (%) Rectum MC Corvus Bladder Prostate Monte Carlo Gy Corvus Dose (Gy) 90 7
8 100 T11-L1 Vertebra CyberKnife Case Study Volume (%) Cord MC Corvus GTV Dose (Gy) Comparison of Dose Distribution Variation of Dose Kernels with Density 12.5 cm depth 5 cm depth High-density medium Low-density medium 8
9 d=3.3cm calculated d=3.3cm measured d=10cm calculated d=10cm measured Variation of Dose Kernels with Density Beamlet Dose Distributions High-density medium Low-density medium FSPB Monte Carlo High-density medium The accuracy depends on how it is implemented. Pawlicki and Ma (2001) Effect of Couch Bar on IMRT Dose Distribution Attenuation for 18 MV photons Other Applications for Advanced RT Relative Dose (%) 20 Without bar Off-axis Distance (cm) MC vs film measurement Yang et al 2005 With bar 9
10 100 Effect of Couch Bar on IMRT Dose Distribution IMRT using 18 MV photons 18MV DVH 100 IMRT using 6 MV photons DVH Data TPS Monte Carlo Volume (%) bladder-no bar rectum-no bar CTV-no bar bladder-with bar rectum-with bar CTV-with bar Volume (%) bladder - no bar rectum - no bar CTV - no bar bladder - with bar rectum - with bar CTV - with bar Dose (cgy) Dose Yang et al 2005 Dose Volume Histogram (H&N) 100 Pencil beam Volume (%) LT Eye Target Monte Carlo MC for Modulated Electron RT Optic chiasm Dose (cgy) 10
11 MU real patient vs.water tank (MC / Water tank= 292 / 256=1.14) MU real patient vs.water tank Target 1,2 MC based MU Target 1,2 water tank based MU Lt eye water tank based MU Rt eye water tank based MU Rt eye MC based MU Lt eye MC based MU Courtesy of Joanna Cygler Internal mammary nodes (MC / Water tank= 210 / 206=1.019) Isodose Distributions of MBRT for Hypofractionated Breast Treatment Courtesy of Joanna Cygler A. Photon IMRT 58.0Gy 56.0Gy 47.3Gy 45.0Gy 42.8Gy 40.5Gy 36.0Gy 31.5Gy 22.5Gy 13.5Gy 4.5Gy B. Electron beam C. Combined dose 11
12 Mixed Beam Breast Plan Statistics and Denoising Optimize IMRT based on a fixed e- field Optimize IMRT and the e- field weight Xiong et al (PMB 2004) Optimize IMRT and MERT Statistics and Smoothing Techniques Dose Prescription Smoothing or denoising is safe at 3-4% statistical level 12
13 CTV or PTV Dose Prescription Dose to what medium? 4% stats 2mm σ 4mm σ 9% 5% 3% 1% Dose prescription based on DVH and isodose distributions Dose to Water or Dose to Medium? Summary MC dose calculation is becoming a practical tool for advanced radiotherapy treatments The accuracy of MC dose calculation depends on the implementation The potential of MC dose calculation remains to be explored 13
14 Acknowledgments The FCCC/Stanford Monte Carlo Team Charlie Ma Bob Price Lili Chen Eugene Fourkal Jinsheng Li Lu Wang Yan Chen James Fan Teh Lin Max Jin Steve Jiang Todd Pawlicki Jun Deng Bilal Shahine Ajay Kapur Michael Lee Qianyi Xu Iavor Veltchev Alain Tafo Ahmed ElDib Francis Tang Sotirios Stathakis Jay Chen Wei Luo Jie Yang Lihong Qin Meisong Ding Omar Chibani Grisel Mora Thai Bing Nguyen William Xiong Antonio Leal Freek Du Plessis Thank You FCCC Monte Carlo course, April
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