Literature DB >> 26418216

An analytic linear accelerator source model for GPU-based Monte Carlo dose calculations.

Zhen Tian1, Yongbao Li, Michael Folkerts, Feng Shi, Steve B Jiang, Xun Jia.   

Abstract

Recently, there has been a lot of research interest in developing fast Monte Carlo (MC) dose calculation methods on graphics processing unit (GPU) platforms. A good linear accelerator (linac) source model is critical for both accuracy and efficiency considerations. In principle, an analytical source model should be more preferred for GPU-based MC dose engines than a phase-space file-based model, in that data loading and CPU-GPU data transfer can be avoided. In this paper, we presented an analytical field-independent source model specifically developed for GPU-based MC dose calculations, associated with a GPU-friendly sampling scheme. A key concept called phase-space-ring (PSR) was proposed. Each PSR contained a group of particles that were of the same type, close in energy and reside in a narrow ring on the phase-space plane located just above the upper jaws. The model parameterized the probability densities of particle location, direction and energy for each primary photon PSR, scattered photon PSR and electron PSR. Models of one 2D Gaussian distribution or multiple Gaussian components were employed to represent the particle direction distributions of these PSRs. A method was developed to analyze a reference phase-space file and derive corresponding model parameters. To efficiently use our model in MC dose calculations on GPU, we proposed a GPU-friendly sampling strategy, which ensured that the particles sampled and transported simultaneously are of the same type and close in energy to alleviate GPU thread divergences. To test the accuracy of our model, dose distributions of a set of open fields in a water phantom were calculated using our source model and compared to those calculated using the reference phase-space files. For the high dose gradient regions, the average distance-to-agreement (DTA) was within 1 mm and the maximum DTA within 2 mm. For relatively low dose gradient regions, the root-mean-square (RMS) dose difference was within 1.1% and the maximum dose difference within 1.7%. The maximum relative difference of output factors was within 0.5%. Over 98.5% passing rate was achieved in 3D gamma-index tests with 2%/2 mm criteria in both an IMRT prostate patient case and a head-and-neck case. These results demonstrated the efficacy of our model in terms of accurately representing a reference phase-space file. We have also tested the efficiency gain of our source model over our previously developed phase-space-let file source model. The overall efficiency of dose calculation was found to be improved by ~1.3-2.2 times in water and patient cases using our analytical model.

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Year:  2015        PMID: 26418216     DOI: 10.1088/0031-9155/60/20/7941

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


  4 in total

1.  Moving GPU-OpenCL-based Monte Carlo dose calculation toward clinical use: Automatic beam commissioning and source sampling for treatment plan dose calculation.

Authors:  Zhen Tian; Yongbao Li; Nima Hassan-Rezaeian; Steve B Jiang; Xun Jia
Journal:  J Appl Clin Med Phys       Date:  2017-02-16       Impact factor: 2.102

2.  The effect of density overrides on magnetic resonance-guided radiation therapy planning for lung cancer.

Authors:  Oliver Schrenk; Claudia Katharina Spindeldreier; Daniela Schmitt; Falk Roeder; Mark Bangert; Lucas Norberto Burigo; Asja Pfaffenberger
Journal:  Phys Imaging Radiat Oncol       Date:  2018-11-22

3.  Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy.

Authors:  Haibin Chen; Zichun Zhong; Yiwei Yang; Jiawei Chen; Linghong Zhou; Xin Zhen; Xuejun Gu
Journal:  Sci Rep       Date:  2018-02-27       Impact factor: 4.379

4.  A fast jaw-tracking model for VMAT and IMRT Monte Carlo simulations.

Authors:  Reid Townson; Hilary Egglestone; Sergei Zavgorodni
Journal:  J Appl Clin Med Phys       Date:  2018-05-09       Impact factor: 2.102

  4 in total

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