Hardware Acceleration of an Efficient and Accurate Proton Therapy Monte Carlo

Abstract

Proton radiation therapy is one of the more effective forms of cancer treatment because of the high degree of selectivity afforded by the behavior of energetic protons in matter. But because radiation does not distinguish between tumor cells and healthy body tissue, it is important to insure that the radiation energy is deposited in the appropriate locations within a patient. This is even more important for proton beams because of the concentrated nature of the radiation energy dose they leave in a body. Predicting such dose distributions can be accurately done via complex and slow Monte Carlo based simulation (using tools such as Geant), but such simulators are too slow for use in interactive situations where a doctor is trying to determine the best beams to use for a particular patient. In this paper we report on an accurate but extremely fast Monte Carlo based proton dose distribution simulator code named Jack. The simulator uses the same physics as more complex tools, but leverages massive parallelization and a streamlined code architecture. The paper describes the state of Jack and shows runtime results for it with and without various hardware acceleration techniques. We benchmark Jack against Geant4.9.4.p01, a well established particle transport code, on a water phantom. Future plans are presented at the end for further speed enhancement and model development.