arXiv cs.AI· Yuzhen Ding, Hongying Feng, Jianfeng Zhao, Jason Holmes, Martin Bues, Lisa A. McGee, Jean-Claude M. Rwigema, Nathan Y. Yu, Terence T. Sio, Sameer R. Keole, William W. Wong, Steven E. Schild, Jonathan B. Ashman, Sujay A. Vora, Daniel J. Ma, Samir H. Patel, Wei Liu·· 5 小时前AI 评分32
基于 Diffusion Transformer 的笔束扫描质子治疗剂量去噪通用框架研究
Generalizable dose denoising using diffusion transformer framework for pencil beam scanning proton therapy
AI 导读
研究者开发了基于 diffusion transformer 的剂量去噪框架，用于从低统计量 Monte Carlo 模拟中恢复高质量剂量分布，以加速在线自适应 IMPT 头颈部肿瘤治疗。
正文 · 原文
Authors:Yuzhen Ding, Hongying Feng, Jianfeng Zhao, Jason Holmes, Martin Bues, Lisa A. McGee, Jean-Claude M. Rwigema, Nathan Y. Yu, Terence T. Sio, Sameer R. Keole, William W. Wong, Steven E. Schild, Jonathan B. Ashman, Sujay A. Vora, Daniel J. Ma, Samir H. Patel, Wei Liu
Abstract:Background and purpose: Intensity-modulated proton therapy (IMPT) enables conformal dose delivery for head and neck (H&N) cancers. However, its sensitivity to inter-fractional anatomical changes motivates online adaptations, where rapid and accurate Monte Carlo (MC) dose calculation remains a major bottleneck. Low-statistics MC simulations reduce computation time but introduce noise. This study aimed to develop a diffusion-transformer framework to recover high-statistics dose distributions from noisy MC simulations. Materials and methods: A diffusion-transformer-based dose denoising framework was developed. Eighty H&N patients were used for training and additional 10 H&N patients and 30 patients with lung, breast, and prostate cancers (10 per site) were used for evaluation. Noisy and high-statistics dose maps were generated using MCsquare. The model was trained using noisy dose maps and CT images as inputs and high-statistics dose maps as ground truth, with mean squared error, residual, regional mean absolute error (MAE), and dose-volume histogram (DVH) losses. Performance was evaluated using MAE, 3D gamma passing rates, and DVH indices. Results: Mean whole-body MAE ranged from 0.08 +/- 0.06 to 0.20 +/- 0.11 Gy (Relative Biological Effectiveness, RBE) (RBE = 1.1) across test cohorts. Mean whole-body 3 %/2 mm gamma passing rates ranged from 95.8 +/- 0.8 % to 97.6 +/- 0.9 %. DVH indices from high-statistics and predicted dose distributions showed close agreement for clinical target volumes and organs of interest. Conclusions: The diffusion-transformer-based framework denoised low-statistics MC dose distributions. Despite being trained on H&N data, the model generalized to lung, breast, and prostate cohorts without retraining.
| Subjects: | Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2506.04467 [physics.med-ph] |
| (or arXiv:2506.04467v2 [physics.med-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2506.04467 arXiv-issued DOI via DataCite |
Submission history
From: Yuzhen Ding [view email]
[v1]
Wed, 4 Jun 2025 21:37:15 UTC (1,080 KB)
[v2]
Wed, 28 Oct 2026 20:41:06 UTC (609 KB)
来源：arXiv cs.AI · arxiv.org