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A GPU-based multi-criteria optimization algorithm for HDR brachytherapy.

08:00 EDT 10th April 2019 | BioPortfolio

Summary of "A GPU-based multi-criteria optimization algorithm for HDR brachytherapy."

Currently in HDR brachytherapy planning, a manual fine-tuning of an objective function is necessary to obtain case-specific valid plans. This study intends to facilitate this process by proposing two patient-specific inverse planning algorithms for HDR prostate brachytherapy: knowledge-based multi-criteria optimization (kMCO) and brute-force multi-criteria optimization (bMCO). Two GPU-based optimization engines including simulated annealing (gSA) and a quasi-Newton optimizer (gL-BFGS) were implemented to compute multiple plans in parallel. After evaluating the equivalence and the computation performance of these two optimization engines, gL-BFGS was selected for kMCO and bMCO algorithms. 578 previously treated prostate HDR cases were divided into training set, validation set and test set. In the training set, a set of polynomial regression models were extracted for kMCO algorithm to use prior knowledge and a small number of alternative plans were computed alongside the predicted solution space. On the other hand, a large number of alternative plans were computed for bMCO algorithm to represent the complete Pareto surface. In the test set, kMCO and bMCO plans were compared with the physician-approved clinical plans. Our results indicated that the optimization process is equivalent between gL-BFGS and gSA. The number of clinically valid plans was 428 (92.6\%) for clinical plans and was 460 (99.6\%) for kMCO plans and was 461 (99.8\%) for bMCO plans. The number of valid plans with target $V_{100}$ coverage greater than 95\% was 288 (62.3\%) for clinical plans, 369 (79.9% for kMCO plans, and 404 (87.4\%) for bMCO plans. The mean plan generation time was 8.8s for kMCO, and 9.3s for bMCO. In conclusion, two ultra-fast and robust multi-criteria optimization algorithms were implemented for HDR prostate brachytherapy. A large-scale comparison against physician approved clinical plans showed that treatment plan quality could be improved and plan generation time could be significantly reduced with the proposed GPU-based MCO algorithms.

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This article was published in the following journal.

Name: Physics in medicine and biology
ISSN: 1361-6560
Pages:

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