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GPU-based ultrafast IMRT plan optimization

Chunhua Men,Xuejun Gu,Dongju Choi,Amitava Majumdar,Ziyi Zheng,Klaus Mueller,Steve B. Jiang
Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92037, USA
Physics in Medicine and Biology, Vol. 54, No. 21. (07 November 2009), pp. 6565-6573

@article{men2009gpu,

   title={GPU-based ultrafast IMRT plan optimization},

   author={Men, C. and Gu, X. and Choi, D. and Majumdar, A. and Zheng, Z. and Mueller, K. and Jiang, S.B.},

   journal={Physics in medicine and biology},

   volume={54},

   pages={6565},

   year={2009},

   publisher={IOP Publishing}

}

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The widespread adoption of on-board volumetric imaging in cancer radiotherapy has stimulated research efforts to develop online adaptive radiotherapy techniques to handle the inter-fraction variation of the patient’s geometry. Such efforts face major technical challenges to perform treatment planning in real time. To overcome this challenge, we are developing a supercomputing online re-planning environment (SCORE) at the University of California, San Diego (UCSD). As part of the SCORE project, this paper presents our work on the implementation of an intensity-modulated radiation therapy (IMRT) optimization algorithm on graphics processing units (GPUs). We adopt a penalty-based quadratic optimization model, which is solved by using a gradient projection method with Armijo’s line search rule. Our optimization algorithm has been implemented in CUDA for parallel GPU computing as well as in C for serial CPU computing for comparison purpose. A prostate IMRT case with various beamlet and voxel sizes was used to evaluate our implementation. On an NVIDIA Tesla C1060 GPU card, we have achieved speedup factors of 20–40 without losing accuracy, compared to the results from an Intel Xeon 2.27 GHz CPU. For a specific nine-field prostate IMRT case with 5 × 5 mm 2 beamlet size and 2.5 × 2.5 × 2.5 mm 3 voxel size, our GPU implementation takes only 2.8 s to generate an optimal IMRT plan. Our work has therefore solved a major problem in developing online re-planning technologies for adaptive radiotherapy.
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