Fast multipurpose Monte Carlo simulation for proton therapy using multi‐ and many‐core CPU architectures. Issue 4 (14th March 2016)

• Record Type: Journal Article
• Authors: Souris, Kevin; Lee, John Aldo; Sterpin, Edmond
• Is Part Of: Medical physics. Volume 43:Issue 4(2016)
• Page Start: 1700
• Page End: 1712
• Publication Date: 2016-03-14
• Subjects: dosimetry -- microprocessor chips -- Monte Carlo methods -- radiation therapy; Dose‐volume analysis -- Dosimetry/exposure assessment -- Monte Carlo methods; Radiation therapy -- Scintigraphy; Monte Carlo simulation -- proton therapy -- Xeon Phi -- high performance computing -- dose calculation; Protons -- Collisional energy loss -- Nuclear interactions -- Databases -- Nuclear structure models -- Tissues -- Computer hardware -- Monte Carlo algorithms -- Proton therapy; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4943377
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Accuracy in proton therapy treatment planning can be improved using Monte Carlo (MC) simulations. However the long computation time of such methods hinders their use in clinical routine. This work aims to develop a fast multipurpose Monte Carlo simulation tool for proton therapy using massively parallel central processing unit (CPU) architectures. Methods: A new Monte Carlo, called MCsquare (many‐core Monte Carlo), has been designed and optimized for the last generation of Intel Xeon processors and Intel Xeon Phi coprocessors. These massively parallel architectures offer the flexibility and the computational power suitable to MC methods. The class‐II condensed history algorithm of MCsquare provides a fast and yet accurate method of simulating heavy charged particles such as protons, deuterons, and alphas inside voxelized geometries. Hard ionizations, with energy losses above a user‐specified threshold, are simulated individually while soft events are regrouped in a multiple scattering theory. Elastic and inelastic nuclear interactions are sampled from ICRU 63 differential cross sections, thereby allowing for the computation of prompt gamma emission profiles. MCsquare has been benchmarked with thegate/geant4 Monte Carlo application for homogeneous and heterogeneous geometries. Results: Comparisons withgate/geant4 for various geometries show deviations within 2%–1 mm. In spite of the limited memory bandwidth of the coprocessor simulation time is below 25 s for 10 7 primary 200 MeV protons in average soft tissues using all Xeon Phi and CPU resources embedded in a single desktop unit. Conclusions: MCsquare exploits the flexibility of CPU architectures to provide a multipurpose MC simulation tool. Optimized code enables the use of accurate MC calculation within a reasonable computation time, adequate for clinical practice. MCsquare also simulates prompt gamma emission and can thus be used also for in vivo range verification.