A coarse-to-fine framework combining deep learning and Monte Carlo for BNCT patient position optimization toward inverse planning.

backgroundBoron Neutron Capture Therapy (BNCT) utilizes high linear energy transfer (LET) charged particles from the PURPOSE: We propose an automated patient position optimization framework for BNCT to address these limitations. This method overcomes the computational bottleneck of MC simulations by integrating a rapid deep learning (DL) dose prediction model with a high-fidelity GPU-accelerated MC engine, enabling efficient inverse planning.

methodsWe developed a hybrid coarse-to-fine optimization workflow driven by the Trust Region Bayesian Optimization (TuRBO) algorithm. In the coarse stage, a 3D U-Net architecture rapidly identifies promising patient positions. In the fine stage, the solution is refined within a restricted parameter bound using the GPU-accelerated MC engine. We evaluated the framework on the GLIS-RT open dataset (229 glioblastoma patients, split into train, validation, and test sets at a 70:20:10 ratio).

resultsOptimizing the patient position with TuRBO improved target dose and homogeneity over a non-optimized baseline, reaching equivalent plan quality about 5.65× faster than an exhaustive grid search. In the coarse stage, the DL dose predictor ran about 37× faster than the high-fidelity MC. The coarse-to-fine workflow then matched high-fidelity MC plan quality while cutting optimization time by a factor of 2.7, outperforming optimization driven by the DL predictor alone. Two-port plans required no change to the method, where the added geometric freedom raised the mean target dose from 59.96% to 74.91% and lowered the homogeneity index (HI) from 1.226 to 0.647.

conclusionsWe successfully developed a Bayesian optimization (BO) framework for BNCT patient positioning. The proposed coarse-to-fine approach effectively balances computational speed with accuracy, offering a practical way toward automated inverse planning in BNCT.