SU-F-BRD-02: Application of ARCHERRT-- A GPU-Based Monte Carlo Dose Engine for Radiation Therapy -- to Tomotherapy and Patient-Independent IMRT

Authors


Abstract

Purpose:

As a module of ARCHER -- Accelerated Radiation-transport Computations in Heterogeneous EnviRonments, ARCHERRT is designed for RadioTherapy (RT) dose calculation. This paper describes the application of ARCHERRT on patient-dependent TomoTherapy and patient-independent IMRT. It also conducts a “fair” comparison of different GPUs and multicore CPU.

Methods:

The source input used for patient-dependent TomoTherapy is phase space file (PSF) generated from optimized plan. For patient-independent IMRT, the open filed PSF is used for different cases. The intensity modulation is simulated by fluence map. The GEANT4 code is used as benchmark. DVH and gamma index test are employed to evaluate the accuracy of ARCHERRT code. Some previous studies reported misleading speedups by comparing GPU code with serial CPU code. To perform a fairer comparison, we write multi-thread code with OpenMP to fully exploit computing potential of CPU. The hardware involved in this study are a 6-core Intel E5-2620 CPU and 6 NVIDIA M2090 GPUs, a K20 GPU and a K40 GPU.

Results:

Dosimetric results from ARCHERRT and GEANT4 show good agreement. The 2%/2mm gamma test pass rates for different clinical cases are 97.2% to 99.7%. A single M2090 GPU needs 50~79 seconds for the simulation to achieve a statistical error of 1% in the PTV. The K40 card is about 1.7∼1.8 times faster than M2090 card. Using 6 M2090 card, the simulation can be finished in about 10 seconds. For comparison, Intel E5-2620 needs 507∼879 seconds for the same simulation.

Conclusion:

We successfully applied ARCHERRT to Tomotherapy and patient-independent IMRT, and conducted a fair comparison between GPU and CPU performance. The ARCHERRT code is both accurate and efficient and may be used towards clinical applications.

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