Vision Transformer model-based dose prediction and beam angle optimization for BNCT

Abstract Objective . A good boron neutron capture therapy (BNCT) treatment plan, which can deliver higher tumor dose and lower doses to organs at risk (OAR), critically depends on the accuracy of dose prediction and optimization strategy. Existing clinical treatment planning systems mainly use Monte Carlo (MC) simulations. These simulations offer high dosimetric accuracy but are computationally costly and compromise planning efficiency. To overcome this limitation, we aim to develop an improved neural network model that can efficiently and accurately predict BNCT dose distributions under different beam angles, thereby facilitating treatment plan optimization. Approach . We propose a deep learning framework that integrates dose prediction with Bayesian optimization (BO) for beam angle selection. A 3D Vision Transformer backbone captures long-range spatial dependencies, while a Mamba module enhances local feature extraction. A region of interest–guided attention mechanism further directs the model’s focus toward gross tumor volume (GTV) and skin. Predicted doses are incorporated into BO to identify the optimal beam conditions. Main results . On a clinical dataset, the proposed model achieved a mean absolute error (MAE) below 0.6 Gy and mean absolute percentage error (MAPE) below 2% for GTV; for skin, MAE was under 0.15 Gy and MAPE below 3.5%. The average gamma passing rates exceeded 90% (2 mm/2%) and 97% (3 mm/3%). After optimization, the minimum voxel dose of the GTV increased by an average of 1.8 Gy, while the maximum voxel dose of the OARs did not increase. Significance . The proposed method has accurate dose prediction and efficient optimization ability, with results validated by MC simulations. It offers a potential application for clinical automated BNCT treatment planning design and optimization.