TH-A-19A-08: Intel Xeon Phi Implementation of a Fast Multi-Purpose Monte Carlo Simulation for Proton Therapy




Recent studies have demonstrated the capability of graphics processing units (GPUs) to compute dose distributions using Monte Carlo (MC) methods within clinical time constraints. However, GPUs have a rigid vectorial architecture that favors the implementation of simplified particle transport algorithms, adapted to specific tasks. Our new, fast, and multipurpose MC code, named MCsquare, runs on Intel Xeon Phi coprocessors. This technology offers 60 independent cores, and therefore more flexibility to implement fast and yet generic MC functionalities, such as prompt gamma simulations.


MCsquare implements several models and hence allows users to make their own tradeoff between speed and accuracy. A 200 MeV proton beam is simulated in a heterogeneous phantom using Geant4 and two configurations of MCsquare. The first one is the most conservative and accurate. The method of fictitious interactions handles the interfaces and secondary charged particles emitted in nuclear interactions are fully simulated. The second, faster configuration simplifies interface crossings and simulates only secondary protons after nuclear interaction events. Integral depth-dose and transversal profiles are compared to those of Geant4. Moreover, the production profile of prompt gammas is compared to PENH results.


Integral depth dose and transversal profiles computed by MCsquare and Geant4 are within 3%. The production of secondaries from nuclear interactions is slightly inaccurate at interfaces for the fastest configuration of MCsquare but this is unlikely to have any clinical impact. The computation time varies between 90 seconds for the most conservative settings to merely 59 seconds in the fastest configuration. Finally prompt gamma profiles are also in very good agreement with PENH results.


Our new, fast, and multi-purpose Monte Carlo code simulates prompt gammas and calculates dose distributions in less than a minute, which complies with clinical time constraints. It has been successfully validated with Geant4.

This work has been financialy supported by InVivoIGT, a public/private partnership between UCL and IBA.