  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 9-field
prostate IMRT case with 5x5 mm^2 beamlet size and 2.5x2.5x2.5 mm^3 voxel size,
our GPU implementation takes only 2.8 seconds to generate an optimal IMRT plan.
Our work has therefore solved a major problem in developing online re-planning
technologies for adaptive radiotherapy.
