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arXiv AI By 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

Generalizable dose denoising using diffusion transformer framework for pencil beam scanning proton therapy

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A diffusion‑transformer framework was developed to denoise low‑statistics Monte Carlo dose distributions for intensity‑modulated proton therapy. Trained on 80 head‑and‑neck patient datasets, the model was evaluated on 10 additional head‑and‑neck cases and 30 patients with lung, breast, and prostate cancers, achieving mean absolute errors of 0.08–0.20 Gy and 3 %/2 mm gamma passing rates above 95 %. The denoised dose maps closely matched high‑statistics reference distributions in dose‑volume histogram metrics for clinical targets and organs at risk.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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