Literature DB >> 28140352

Initial development of goCMC: a GPU-oriented fast cross-platform Monte Carlo engine for carbon ion therapy.

Nan Qin1, Marco Pinto, Zhen Tian, Georgios Dedes, Arnold Pompos, Steve B Jiang, Katia Parodi, Xun Jia.   

Abstract

Monte Carlo (MC) simulation is considered as the most accurate method for calculation of absorbed dose and fundamental physics quantitin class="Chemical">es related to biological effects in carbon ion therapy. To improve its computational efficiency, we have developed a GPU-oriented fast MC package named goCMC, for carbon therapy. goCMC simulates particle transport in voxelized geometry with kinetic energy up to 450 MeV u-1. Class II condensed history simulation scheme with a continuous slowing down approximation was employed. Energy straggling and multiple scattering were modeled. δ-electrons were terminated with their energy locally deposited. Four types of nuclear interactions were implemented in goCMC, i.e. carbon-hydrogen, carbon-carbon, carbon-oxygen and carbon-calcium inelastic collisions. Total cross section data from Geant4 were used. Secondary particles produced in these interactions were sampled according to particle yield with energy and directional distribution data derived from Geant4 simulation results. Secondary charged particles were transported following the condensed history scheme, whereas secondary neutral particles were ignored. goCMC was developed under OpenCL framework and is executable on different platforms, e.g. GPU and multi-core CPU. We have validated goCMC with Geant4 in cases with different beam energy and phantoms including four homogeneous phantoms, one heterogeneous half-slab phantom, and one patient case. For each case [Formula: see text] carbon ions were simulated, such that in the region with dose greater than 10% of maximum dose, the mean relative statistical uncertainty was less than 1%. Good agreements for dose distributions and range estimations between goCMC and Geant4 were observed. 3D gamma passing rates with 1%/1 mm criterion were over 90% within 10% isodose line except in two extreme cases, and those with 2%/1 mm criterion were all over 96%. Efficiency and code portability were tested with different GPUs and CPUs. Depending on the beam energy and voxel size, the computation time to simulate [Formula: see text] carbons was 9.9-125 s, 2.5-50 s and 60-612 s on an AMD Radeon GPU card, an NVidia GeForce GTX 1080 GPU card and an Intel Xeon E5-2640 CPU, respectively. The combined accuracy, efficiency and portability make goCMC attractive for research and clinical applications in carbon ion therapy.

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Year:  2017        PMID: 28140352      PMCID: PMC5730973          DOI: 10.1088/1361-6560/aa5d43

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  45 in total

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Authors:  Xun Jia; Xuejun Gu; Yan Jiang Graves; Michael Folkerts; Steve B Jiang
Journal:  Phys Med Biol       Date:  2011-10-21       Impact factor: 3.609

2.  Reducing the sensitivity of IMPT treatment plans to setup errors and range uncertainties via probabilistic treatment planning.

Authors:  Jan Unkelbach; Thomas Bortfeld; Benjamin C Martin; Martin Soukup
Journal:  Med Phys       Date:  2009-01       Impact factor: 4.071

3.  Monte Carlo simulations to support start-up and treatment planning of scanned proton and carbon ion therapy at a synchrotron-based facility.

Authors:  K Parodi; A Mairani; S Brons; B G Hasch; F Sommerer; J Naumann; O Jäkel; T Haberer; J Debus
Journal:  Phys Med Biol       Date:  2012-05-23       Impact factor: 3.609

4.  A study of potential numerical pitfalls in GPU-based Monte Carlo dose calculation.

Authors:  Vincent Magnoux; Benoît Ozell; Éric Bonenfant; Philippe Després
Journal:  Phys Med Biol       Date:  2015-06-10       Impact factor: 3.609

5.  Integration and evaluation of automated Monte Carlo simulations in the clinical practice of scanned proton and carbon ion beam therapy.

Authors:  J Bauer; F Sommerer; A Mairani; D Unholtz; R Farook; J Handrack; K Frey; T Marcelos; T Tessonnier; S Ecker; B Ackermann; M Ellerbrock; J Debus; K Parodi
Journal:  Phys Med Biol       Date:  2014-07-31       Impact factor: 3.609

Review 6.  GPU-based high-performance computing for radiation therapy.

Authors:  Xun Jia; Peter Ziegenhein; Steve B Jiang
Journal:  Phys Med Biol       Date:  2014-02-03       Impact factor: 3.609

7.  A Monte Carlo dose calculation algorithm for proton therapy.

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Journal:  Med Phys       Date:  2004-08       Impact factor: 4.071

8.  Sub-second high dose rate brachytherapy Monte Carlo dose calculations with bGPUMCD.

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Review 9.  Range uncertainties in proton therapy and the role of Monte Carlo simulations.

Authors:  Harald Paganetti
Journal:  Phys Med Biol       Date:  2012-05-09       Impact factor: 3.609

10.  Monte Carlo-based parametrization of the lateral dose spread for clinical treatment planning of scanned proton and carbon ion beams.

Authors:  Katia Parodi; Andrea Mairani; Florian Sommerer
Journal:  J Radiat Res       Date:  2013-07       Impact factor: 2.724

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  6 in total

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2.  Improving Proton Dose Calculation Accuracy by Using Deep Learning.

Authors:  Chao Wu; Dan Nguyen; Yixun Xing; Ana Barragan Montero; Jan Schuemann; Haijiao Shang; Yuehu Pu; Steve Jiang
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3.  Fixed Beamline Optimization for Intensity Modulated Carbon-Ion Therapy.

Authors:  Pavitra Ramesh; Hengjie Liu; Wenbo Gu; Ke Sheng
Journal:  IEEE Trans Radiat Plasma Med Sci       Date:  2021-06-25

4.  Full Monte Carlo-Based Biologic Treatment Plan Optimization System for Intensity Modulated Carbon Ion Therapy on Graphics Processing Unit.

Authors:  Nan Qin; Chenyang Shen; Min-Yu Tsai; Marco Pinto; Zhen Tian; Georgios Dedes; Arnold Pompos; Steve B Jiang; Katia Parodi; Xun Jia
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-09-12       Impact factor: 7.038

5.  A Data-Driven Fragmentation Model for Carbon Therapy GPU-Accelerated Monte-Carlo Dose Recalculation.

Authors:  Micol De Simoni; Giuseppe Battistoni; Angelica De Gregorio; Patrizia De Maria; Marta Fischetti; Gaia Franciosini; Michela Marafini; Vincenzo Patera; Alessio Sarti; Marco Toppi; Giacomo Traini; Antonio Trigilio; Angelo Schiavi
Journal:  Front Oncol       Date:  2022-03-25       Impact factor: 6.244

6.  FRoG-A New Calculation Engine for Clinical Investigations with Proton and Carbon Ion Beams at CNAO.

Authors:  KyungDon Choi; Stewart B Mein; Benedikt Kopp; Giuseppe Magro; Silvia Molinelli; Mario Ciocca; Andrea Mairani
Journal:  Cancers (Basel)       Date:  2018-10-23       Impact factor: 6.639

  6 in total

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