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This paper applies a diffusion-transformer model to denoise fast, low-statistics Monte Carlo dose maps for proton therapy, so clinicians can get near-high-fidelity dose estimates during online plan adaptation. The reported gains are measured against high-statistics references, and the model was trained only on head and neck cases, so the generalization to lung, breast, and prostate is a notable but still limited result. Readers should weigh it as a validated research step toward clinical use, not a deployed system.
Generalizable dose denoising using diffusion transformer framework for pencil beam scanning proton therapy
arXiv:2506.04467v2 Announce Type: replace-cross 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.
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.
Medical Physics (physics.med-ph); Artificial Intelligence (cs.AI)
arXiv:2506.04467 [physics.med-ph]
(or arXiv:2506.04467v2 [physics.med-ph] for this version)
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