发表机构
Image X Institute, The University of Sydney（悉尼大学图像X研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对DoseRAD2026挑战赛提出BEAM3R框架，结合Mamba-3与物理传输条件实现快速准确的光子和质子剂量计算，在CT数据上取得高伽马通过率，MRI合成CT会降低性能，运行时满足效率要求。
AI 中文摘要
为在DoseRAD2026挑战赛中实现光子控制点和质子束let剂量的准确快速计算，本文提出了BEAM3R，一种在射线视角（BEV）下运行的剂量估计框架。其核心创新是将Mamba-3状态空间深度序列核心与基于物理的传输条件相结合，以对长程深度传输进行建模，无需昂贵的3D卷积。BEAM3R为光子和质子剂量任务共享2D CNN编码器-解码器架构，处理每个平面的BEV切片；质子束let以水等效厚度和剩余射程为条件，编码决定布拉格峰位置的参数；光子模型使用双向Mamba-3核心，以捕获计算点下游材料的剂量贡献，而质子模型使用带有学习到的能量前缀标记的前向核心和布拉格峰细化模块。为减少插值伪影并支持高空间分辨率，本文引入了BEV晶格与CT切片的轴向网格对齐，以及通过子像素相位打包实现的隐式超分辨率表示，该表示通过可微分的Triton加速重采样器评估，该重采样器直接在CT空间中重建打包的三次B样条系数。对于基于MRI的任务，通过带有SwinUNETR主干的基于补丁的条件GAN生成合成CT（sCT）。在DoseRAD2026初步测试集上，CT-to-photon和CT-to-proton模型的1%/1 mm局部伽马通过率分别为96.8%和96.0%，分层计划级平均绝对误差（MAE）分别为0.0041和0.0079；替换为sCT后，光子和质子计划级剂量的伽马通过率降至89.7%和75.4%，分层计划级MAE分别为0.0093和0.0336。标准化运行时方面，CT-to-photon和CT-to-proton预测分别为23.4秒和18.4秒，对应的基于MRI的管道则分别增加至39.7秒和42.8秒。
英文摘要
To enable accurate and rapid photon control point and proton beamlet dose calculation in the DoseRAD2026 challenge, we present BEAM3R, a dose estimation framework operating in beam's-eye-view (BEV). Our core innovation combines a Mamba-3 state-space depth-sequence core with physics-based transport conditioning to model long-range depth transport without expensive 3D convolutions. BEAM3R shares a 2D CNN encoder-decoder architecture for photon and proton dose tasks, processing per-plane BEV slices. Proton beamlets are conditioned on water equivalent thickness and remaining range, encoding the parameters determining Bragg peak position. Photon models use a bidirectional Mamba-3 core to capture dose contributions from materials downstream of the calculation point, while the proton model uses a forward core with learned energy-prefix tokens and a Bragg-peak refinement module. To reduce interpolation artifacts and support high spatial resolution, we introduce axial grid alignment of BEV lattices with CT slices and an implicit super-resolution representation via sub-pixel phase packing, evaluated by a differentiable Triton-accelerated resampler that reconstructs packed cubic B-spline coefficients directly in CT space. For MRI-based tasks, synthetic CTs (sCT) are generated by a patch-based conditional GAN with a SwinUNETR backbone. On the preliminary DoseRAD2026 test set, CT-to-photon and CT-to-proton models achieved 1%/1 mm local gamma pass rates of 96.8% and 96.0%, with stratified plan-level MAEs of 0.0041 and 0.0079. Substituting sCT reduced gamma pass rates to 89.7% for photon and 75.4% proton plan level doses, with stratified plan-level MAEs of 0.0093 and 0.0336. Standardised runtimes were 23.4 s and 18.4 s for CT-to-photon and CT-to-proton prediction, increasing to 39.7 s and 42.8 s for the corresponding MRI-based pipelines.
Comments14 pages, 5 figures
