A fast GPU‐based Monte Carlo simulation of proton transport with detailed modeling of nonelastic interactions. Issue 6 (21st May 2015)

• Record Type: Journal Article
• Authors: Wan Chan Tseung, H.; Ma, J.; Beltran, C.
• Is Part Of: Medical physics. Volume 42:Issue 6(2015)Part 1
• Page Start: 2967
• Page End: 2978
• Publication Date: 2015-05-21
• Subjects: dosimetry -- graphics processing units -- Monte Carlo methods -- phantoms -- radiation therapy; Monte Carlo simulations -- Dosimetry/exposure assessment -- Dose‐volume analysis -- Applications; Radiation therapy -- Processor architectures; Processor configuration, e.g. pipelining -- Scintigraphy; proton therapy -- cuda -- GPU -- Monte Carlo -- nonelastic; Protons -- Monte Carlo methods -- Nuclear interactions -- Collisional energy loss -- Neutrons -- Dosimetry -- Multiple scattering -- Kinematics -- Elasticity -- Elastic collisions; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4921046
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Very fast Monte Carlo (MC) simulations of proton transport have been implemented recently on graphics processing units (GPUs). However, these MCs usually use simplified models for nonelastic proton–nucleus interactions. Our primary goal is to build a GPU‐based proton transport MC with detailed modeling of elastic and nonelastic proton–nucleus collisions. Methods: Using thecuda framework, the authors implemented GPU kernels for the following tasks: (1) simulation of beam spots from our possible scanning nozzle configurations, (2) proton propagation through CT geometry, taking into account nuclear elastic scattering, multiple scattering, and energy loss straggling, (3) modeling of the intranuclear cascade stage of nonelastic interactions when they occur, (4) simulation of nuclear evaporation, and (5) statistical error estimates on the dose. To validate our MC, the authors performed (1) secondary particle yield calculations in proton collisions with therapeutically relevant nuclei, (2) dose calculations in homogeneous phantoms, (3) recalculations of complex head and neck treatment plans from a commercially available treatment planning system, and compared withgeant 4.9.6p2/TOPAS. Results: Yields, energy, and angular distributions of secondaries from nonelastic collisions on various nuclei are in good agreement with thegeant 4.9.6p2 Bertini and Binary cascade models. The 3D‐gamma pass rate at 2%‐2 mm for treatment plan simulations is typically 98%. The net computational time on a NVIDIA GTX680 card, including all CPU–GPU data transfers, is ∼20 s for 1 × 10 7 proton histories. Conclusions: Our GPU‐based MC is the first of its kind to include a detailed nuclear model to handle nonelastic interactions of protons with any nucleus. Dosimetric calculations are in very good agreement withgeant 4.9.6p2/TOPAS. Our MC is being integrated into a framework to perform fast routine clinical QA of pencil‐beam based treatment plans, and is being used as the dose calculation engine in a clinically applicable MC‐based IMPT treatment planning system. The detailed nuclear modeling will allow us to perform very fast linear energy transfer and neutron dose estimates on the GPU.