Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures

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 massi...

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Veröffentlicht in:Medical physics (Lancaster) Jg. 43; H. 4; S. 1700 - 1712
Hauptverfasser: Souris, Kevin, Lee, John Aldo, Sterpin, Edmond
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States American Association of Physicists in Medicine 01.04.2016
Schlagworte:
60 APPLIED LIFE SCIENCES
ACCURACY
ALGORITHMS
ANIMAL TISSUES
BENCHMARKS
Collisional energy loss
Computer hardware
COMPUTERIZED SIMULATION
Computers
DEUTERONS
dose calculation
Dose‐volume analysis
dosimetry
Dosimetry/exposure assessment
ENERGY LOSSES
high performance computing
ICRU
IN VIVO
microprocessor chips
Monte Carlo algorithms
MONTE CARLO METHOD
Monte Carlo methods
Monte Carlo simulation
MULTIPLE SCATTERING
Nuclear interactions
Nuclear structure models
Phantoms, Imaging
PROTON BEAMS
Proton Therapy
Protons
RADIATION PROTECTION AND DOSIMETRY
radiation therapy
RADIOTHERAPY
Scintigraphy
Time Factors
Tissues
Water
Xeon Phi
Xeon Phi
dose calculation
proton therapy
high performance computing
Monte Carlo simulation
ISSN:0094-2405, 2473-4209
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Zusammenfassung: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 the gate/geant4 Monte Carlo application for homogeneous and heterogeneous geometries. Results: Comparisons with gate/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 107 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.
Bibliographie:kevin.souris@uclouvain.be
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ISSN:0094-2405
2473-4209
DOI:10.1118/1.4943377