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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.04375 |
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Table of Contents:
- Accurate 3D dose calculation for Pencil Beam Scanning Proton Therapy (PBSPT) is typically performed with Monte Carlo (MC) engines, but their runtimes limit adaptive workflows and repeated evaluations. Current deep-learning proton dose engines often require orthogonality between proton rays and the CT grid, forcing computationally expensive beamlet-wise 3D reinterpolation. We propose the Angle-dependent Dose Transformer Algorithm (ADoTA), which eliminates grid rotation by augmenting the model input with a fast analytical beamlet-shape projection that explicitly encodes beam direction. The model was trained on CT data from 108 patients to predict beamlet dose distributions for initial energies of $70$--$270\,\mathrm{MeV}$ over an $80\times110\,\mathrm{mm}^2$ field, and tested on an independent cohort of 50 patients. On the test set, gamma pass rates $(1\%,3\,\mathrm{mm})$ were $99.40\pm0.86\%$ (thorax) and $99.87\pm0.23\%$ (abdomen/pelvis). Single-beamlet inference took $1.72\pm0.8\,\mathrm{ms}$. By avoiding reinterpolation, end-to-end 3D dose computation was reduced by $\approx86\%$ relative to the fastest published reinterpolation-based methods. For full treatment plans, gamma pass rates $Γ(2\%,2\,\mathrm{mm})$ with a 10\% dose cut-off reached $98.4\%$ (lung) and $98.9\%$ (prostate). ADoTA provides an angle-aware deep-learning proton dose engine that preserves MC-level accuracy across heterogeneous anatomies while substantially reducing computational overhead.