{"ID":2867940,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18378","arxiv_id":"2509.18378","title":"Neural Network-Driven Direct CBCT-Based Dose Calculation for Head-and-Neck Proton Treatment Planning","abstract":"Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios. Traditional CBCT-based dose calculation suffers from image quality limitations, requiring complex correction workflows. This study develops and validates a deep learning approach for direct proton dose calculation from CBCT images using extended Long Short-Term Memory (xLSTM) neural networks. A retrospective dataset of 40 head-and-neck cancer patients with paired planning CT and treatment CBCT images was used to train an xLSTM-based neural network (CBCT-NN). The architecture incorporates energy token encoding and beam's-eye-view sequence modelling to capture spatial dependencies in proton dose deposition patterns. Training utilized 82,500 paired beam configurations with Monte Carlo-generated ground truth doses. Validation was performed on 5 independent patients using gamma analysis, mean percentage dose error assessment, and dose-volume histogram comparison. The CBCT-NN achieved gamma pass rates of 95.1 $\\pm$ 2.7% using 2mm/2% criteria. Mean percentage dose errors were 2.6 $\\pm$ 1.4% in high-dose regions ($\u003e$90% of max dose) and 5.9 $\\pm$ 1.9% globally. Dose-volume histogram analysis showed excellent preservation of target coverage metrics (Clinical Target Volume V95% difference: -0.6 $\\pm$ 1.1%) and organ-at-risk constraints (parotid mean dose difference: -0.5 $\\pm$ 1.5%). Computation time is under 3 minutes without sacrificing Monte Carlo-level accuracy. This study demonstrates the proof-of-principle of direct CBCT-based proton dose calculation using xLSTM neural networks. The approach eliminates traditional correction workflows while achieving comparable accuracy and computational efficiency suitable for adaptive protocols.","short_abstract":"Accurate dose calculation on cone beam computed tomography (CBCT) images is essential for modern proton treatment planning workflows, particularly when accounting for inter-fractional anatomical changes in adaptive treatment scenarios. Traditional CBCT-based dose calculation suffers from image quality limitations, requ...","url_abs":"https://arxiv.org/abs/2509.18378","url_pdf":"https://arxiv.org/pdf/2509.18378v1","authors":"[\"Muheng Li\",\"Evangelia Choulilitsa\",\"Lisa Fankhauser\",\"Francesca Albertini\",\"Antony Lomax\",\"Ye Zhang\"]","published":"2025-09-22T20:01:32Z","proceeding":"physics.med-ph","tasks":"[\"physics.med-ph\",\"cs.CV\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}