Evidence map›Paper›PMID 28300376›Full record

ArticleJournal of applied clinical medical physics2017

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

Zhen Tian, Yongbao Li, Nima Hassan-Rezaeian, Steve B Jiang, Xun Jia

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Article in Journal of applied clinical medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Zhen TianDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Yongbao LiDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Nima Hassan-RezaeianDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Steve B JiangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.
Xun JiaDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We have previously developed a GPU-based Monte Carlo (MC) dose engine on the OpenCL platform, named goMC, with a built-in analytical linear accelerator (linac) beam model. In this paper, we report our recent improvement on goMC to move it toward clinical use. First, we have adapted a previously developed automatic beam commissioning approach to our beam model. The commissioning was conducted through an optimization process, minimizing the discrepancies between calculated dose and measurement. We successfully commissioned six beam models built for Varian TrueBeam linac photon beams, including four beams of different energies (6 MV, 10 MV, 15 MV, and 18 MV) and two flattening-filter-free (FFF) beams of 6 MV and 10 MV. Second, to facilitate the use of goMC for treatment plan dose calculations, we have developed an efficient source particle sampling strategy. It uses the pre-generated fluence maps (FMs) to bias the sampling of the control point for source particles already sampled from our beam model. It could effectively reduce the number of source particles required to reach a statistical uncertainty level in the calculated dose, as compared to the conventional FM weighting method. For a head-and-neck patient treated with volumetric modulated arc therapy (VMAT), a reduction factor of ~2.8 was achieved, accelerating dose calculation from 150.9 s to 51.5 s. The overall accuracy of goMC was investigated on a VMAT prostate patient case treated with 10 MV FFF beam. 3D gamma index test was conducted to evaluate the discrepancy between our calculated dose and the dose calculated in Varian Eclipse treatment planning system. The passing rate was 99.82% for 2%/2 mm criterion and 95.71% for 1%/1 mm criterion. Our studies have demonstrated the effectiveness and feasibility of our auto-commissioning approach and new source sampling strategy for fast and accurate MC dose calculations for treatment plans.

Indexed as

Models, TheoreticalMonte Carlo MethodPatient Care PlanningComputer SimulationHead and Neck NeoplasmsHumansMaleParticle AcceleratorsProstatic NeoplasmsRadiotherapy DosageRadiotherapy, Intensity-ModulatedRadiotherapy Planning, Computer-Assistedautomatic beam commissioningGPU-based Monte Carlo dose calculationsource samplingVMAT dose calculation

Identifiers

PMID28300376
PMCPMC5689963

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.