用扩散Transformer去噪低统计量质子治疗剂量图，实现快速高精度计算。
Diffusion Transformer-based Universal Dose Denoising for Pencil Beam Scanning Proton Therapy
- 基于扩散Transformer的去噪框架，输入低统计剂量图与CT图像。
- 跨病种平均误差低于0.38Gy[RBE]，3D伽马通过率超92%。
- 适合需要快速精准剂量计算的自适应放疗场景。
目的：调强质子治疗（IMPT）在头颈部癌中可精准覆盖肿瘤并保护器官，但对解剖变化敏感，需依赖在线自适应放疗（oART），其关键在于通过蒙特卡洛（MC）模拟实现快速准确的剂量计算。减少粒子数虽能加速计算，但会降低精度。为此，本文提出对低统计量MC剂量图进行去噪，以实现快速高质量的剂量生成。方法：构建基于扩散变压器的去噪框架。利用80例头颈部癌患者的IMPT计划与3D CT图像，使用MCsquare生成噪声剂量图（每计划1分钟）和高统计量剂量图（每计划10分钟）。数据经标准化、零填充、归一化，并转换为准高斯分布。测试涵盖10例头颈、10例肺、10例乳腺及10例前列腺癌病例，预处理一致。模型以噪声剂量图与CT图为输入，高统计量剂量图为真实值，采用均方误差（MSE）、残差损失与区域MAE（聚焦于上下10%剂量体素）联合损失训练。性能通过MAE、3D伽马通过率及剂量体积直方图（DVH）指标评估。结果：模型在头颈、肺、乳腺、前列腺癌中的平均绝对误差分别为0.195、0.120、0.172和0.376 Gy[RBE]；所有部位3D伽马通过率（3%/2mm）均超过92%；临床靶区（CTV）与危及器官（OAR）的DVH指标与真实值高度一致。结论：所提出的扩散变压器去噪框架已成功开发，尽管仅在头颈部数据上训练，但在多种疾病部位间表现出良好泛化能力。
原文摘要 · Abstract (English)
Purpose: Intensity-modulated proton therapy (IMPT) offers precise tumor coverage while sparing organs at risk (OARs) in head and neck (H&N) cancer. However, its sensitivity to anatomical changes requires frequent adaptation through online adaptive radiation therapy (oART), which depends on fast, accurate dose calculation via Monte Carlo (MC) simulations. Reducing particle count accelerates MC but degrades accuracy. To address this, denoising low-statistics MC dose maps is proposed to enable fast, high-quality dose generation. Methods: We developed a diffusion transformer-based denoising framework. IMPT plans and 3D CT images from 80 H&N patients were used to generate noisy and high-statistics dose maps using MCsquare (1 min and 10 min per plan, respectively). Data were standardized into uniform chunks with zero-padding, normalized, and transformed into quasi-Gaussian distributions. Testing was done on 10 H&N, 10 lung, 10 breast, and 10 prostate cancer cases, preprocessed identically. The model was trained with noisy dose maps and CT images as input and high-statistics dose maps as ground truth, using a combined loss of mean square error (MSE), residual loss, and regional MAE (focusing on top/bottom 10% dose voxels). Performance was assessed via MAE, 3D Gamma passing rate, and DVH indices. Results: The model achieved MAEs of 0.195 (H&N), 0.120 (lung), 0.172 (breast), and 0.376 Gy[RBE] (prostate). 3D Gamma passing rates exceeded 92% (3%/2mm) across all sites. DVH indices for clinical target volumes (CTVs) and OARs closely matched the ground truth. Conclusion: A diffusion transformer-based denoising framework was developed and, though trained only on H&N data, generalizes well across multiple disease sites.
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