11747

GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy

F. Ammazzalorso, T. Bednarz, U. Jelen
Department of Radiotherapy and Radiation Oncology – Particle Therapy Center, University of Marburg, Marburg, Germany; Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Heidelberg, Germany; CSIRO Mathematics Informatics & Statistics, Sydney, Australia
J. Phys.: Conf. Ser. 489 012043

@inproceedings{ammazzalorso2014gpu,

   title={GPU-accelerated automatic identification of robust beam setups for proton and carbon-ion radiotherapy},

   author={Ammazzalorso, F and Bednarz, T and Jelen, U},

   booktitle={Journal of Physics: Conference Series},

   volume={489},

   number={1},

   pages={012043},

   year={2014},

   organization={IOP Publishing}

}

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We demonstrate acceleration on graphic processing units (GPU) of automatic identification of robust particle therapy beam setups, minimizing negative dosimetric effects of Bragg peak displacement caused by treatment-time patient positioning errors. Our particle therapy research toolkit, RobuR, was extended with OpenCL support and used to implement calculation on GPU of the Port Homogeneity Index, a metric scoring irradiation port robustness through analysis of tissue density patterns prior to dose optimization and computation. Results were benchmarked against an independent native CPU implementation. Numerical results were in agreement between the GPU implementation and native CPU implementation. For 10 skull base cases, the GPU-accelerated implementation was employed to select beam setups for proton and carbon ion treatment plans, which proved to be dosimetrically robust, when recomputed in presence of various simulated positioning errors. From the point of view of performance, average running time on the GPU decreased by at least one order of magnitude compared to the CPU, rendering the GPU-accelerated analysis a feasible step in a clinical treatment planning interactive session. In conclusion, selection of robust particle therapy beam setups can be effectively accelerated on a GPU and become an unintrusive part of the particle therapy treatment planning workflow. Additionally, the speed gain opens new usage scenarios, like interactive analysis manipulation (e.g. constraining of some setup) and re-execution. Finally, through OpenCL portable parallelism, the new implementation is suitable also for CPU-only use, taking advantage of multiple cores, and can potentially exploit types of accelerators other than GPUs.
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