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Title: SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System

Abstract

Purpose: Current CyberKnife treatment planning system (TPS) provided two dose calculation algorithms: Ray-tracing and Monte Carlo. Ray-tracing algorithm is fast, but less accurate, and also can’t handle irregular fields since a multi-leaf collimator system was recently introduced to CyberKnife M6 system. Monte Carlo method has well-known accuracy, but the current version still takes a long time to finish dose calculations. The purpose of this paper is to develop a GPU-based fast C/S dose engine for CyberKnife system to achieve both accuracy and efficiency. Methods: The TERMA distribution from a poly-energetic source was calculated based on beam’s eye view coordinate system, which is GPU friendly and has linear complexity. The dose distribution was then computed by inversely collecting the energy depositions from all TERMA points along 192 collapsed-cone directions. EGSnrc user code was used to pre-calculate energy deposition kernels (EDKs) for a series of mono-energy photons The energy spectrum was reconstructed based on measured tissue maximum ratio (TMR) curve, the TERMA averaged cumulative kernels was then calculated. Beam hardening parameters and intensity profiles were optimized based on measurement data from CyberKnife system. Results: The difference between measured and calculated TMR are less than 1% for all collimators except in the build-upmore » regions. The calculated profiles also showed good agreements with the measured doses within 1% except in the penumbra regions. The developed C/S dose engine was also used to evaluate four clinical CyberKnife treatment plans, the results showed a better dose calculation accuracy than Ray-tracing algorithm compared with Monte Carlo method for heterogeneous cases. For the dose calculation time, it takes about several seconds for one beam depends on collimator size and dose calculation grids. Conclusion: A GPU-based C/S dose engine has been developed for CyberKnife system, which was proven to be efficient and accurate for clinical purpose, and can be easily implemented in TPS.« less

Authors:
; ; ; ; ; ;  [1];  [2];  [3];  [4]
  1. Beihang University, Beijing, Beijing (China)
  2. Southern Medical University, Guangzhou, Guangdong (China)
  3. PLA General Hospital, Beijing, Beijing (China)
  4. 302 Military Hospital, Beijing, Beijing (China)
Publication Date:
OSTI Identifier:
22649183
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; ACCURACY; ALGORITHMS; BEAMS; COLLIMATORS; ENERGY ABSORPTION; ENERGY LOSSES; ENERGY SPECTRA; MONTE CARLO METHOD; RADIATION DOSE DISTRIBUTIONS; RADIOTHERAPY; SURGERY

Citation Formats

Li, Y, Liu, B, Liang, B, Xu, X, Guo, B, Wei, R, Zhou, F, Song, T, Xu, S, and Piao, J. SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System. United States: N. p., 2016. Web. doi:10.1118/1.4956805.
Li, Y, Liu, B, Liang, B, Xu, X, Guo, B, Wei, R, Zhou, F, Song, T, Xu, S, & Piao, J. SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System. United States. doi:10.1118/1.4956805.
Li, Y, Liu, B, Liang, B, Xu, X, Guo, B, Wei, R, Zhou, F, Song, T, Xu, S, and Piao, J. Wed . "SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System". United States. doi:10.1118/1.4956805.
@article{osti_22649183,
title = {SU-F-T-620: Development of a Convolution/Superposition Dose Engine for CyberKnife System},
author = {Li, Y and Liu, B and Liang, B and Xu, X and Guo, B and Wei, R and Zhou, F and Song, T and Xu, S and Piao, J},
abstractNote = {Purpose: Current CyberKnife treatment planning system (TPS) provided two dose calculation algorithms: Ray-tracing and Monte Carlo. Ray-tracing algorithm is fast, but less accurate, and also can’t handle irregular fields since a multi-leaf collimator system was recently introduced to CyberKnife M6 system. Monte Carlo method has well-known accuracy, but the current version still takes a long time to finish dose calculations. The purpose of this paper is to develop a GPU-based fast C/S dose engine for CyberKnife system to achieve both accuracy and efficiency. Methods: The TERMA distribution from a poly-energetic source was calculated based on beam’s eye view coordinate system, which is GPU friendly and has linear complexity. The dose distribution was then computed by inversely collecting the energy depositions from all TERMA points along 192 collapsed-cone directions. EGSnrc user code was used to pre-calculate energy deposition kernels (EDKs) for a series of mono-energy photons The energy spectrum was reconstructed based on measured tissue maximum ratio (TMR) curve, the TERMA averaged cumulative kernels was then calculated. Beam hardening parameters and intensity profiles were optimized based on measurement data from CyberKnife system. Results: The difference between measured and calculated TMR are less than 1% for all collimators except in the build-up regions. The calculated profiles also showed good agreements with the measured doses within 1% except in the penumbra regions. The developed C/S dose engine was also used to evaluate four clinical CyberKnife treatment plans, the results showed a better dose calculation accuracy than Ray-tracing algorithm compared with Monte Carlo method for heterogeneous cases. For the dose calculation time, it takes about several seconds for one beam depends on collimator size and dose calculation grids. Conclusion: A GPU-based C/S dose engine has been developed for CyberKnife system, which was proven to be efficient and accurate for clinical purpose, and can be easily implemented in TPS.},
doi = {10.1118/1.4956805},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = {Wed Jun 15 00:00:00 EDT 2016},
month = {Wed Jun 15 00:00:00 EDT 2016}
}