BEAM3R is a dose estimation framework that operates in beam’s-eye-view (BEV) for both photon and proton beamlet calculations. It uses a Mamba‑3 state‑space depth‑sequence core combined with physics‑based transport conditioning, sharing a 2D CNN encoder‑decoder architecture to process per‑plane BEV slices. The system achieves high accuracy on the DoseRAD2026 challenge, with local gamma pass rates above 96% for CT‑based models and runtimes under 25 s for photon and 18 s for proton predictions.
By Chen Cheng, Michael Ferraro, James Grover, David E J Waddington, Emily Hewson
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.
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
Dose-PlanNet is a physics-guided 3D deep learning architecture that predicts radiotherapy dose distributions for prostate cancer. In a prospective trial, the model achieved target coverage comparable to clinical plans while slightly reducing target homogeneity, yet it significantly improved high‑dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in 11 of 14 Moderate Hypofraction and 9 of 12 SBRT plans, demonstrating that physics-informed deep learning can accelerate radiotherapy workflows without compromising dosimetric quality.
By Ankit Bhattacharjee, Sougata Maity, Santam Chakraborty, Indranil Mallick
arXiv:2606. 29592v1 Announce Type: new Abstract: A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition.
By Can Polat, Erchin Serpedin, Mustafa Kurban, Hasan Kurban
Dose-PlanNet is a physics-guided 3D deep learning model that predicts radiotherapy dose distributions for prostate cancer, aiming to automate complex treatment planning. In a prospective trial, the model achieved comparable target coverage while slightly reducing target homogeneity, yet it significantly improved high-dose organ‑at‑risk sparing. Automated plans met clinical acceptance criteria in most cases across both moderate hypofraction and stereotactic body radiation therapy arms.
A central premise of autonomous scientific imaging is that smarter navigation, whether Bayesian, RL-based, or otherwise adaptive, is the principal lever for sample-efficient acquisition. We present evidence to the contrary in scanning transmission electron microscopy (STEM), an atomic-resolution imaging modality whose every measurement deposits damaging electron dose.
arXiv:2608.28773v1 Announce Type: cross
Abstract: Perceptual losses are widely used in medical image synthesis because they encourage agreement in high-level structure beyond voxel-wise intensity sim...
By Changsheng Fang, Dayang Wang, T. Campbell Arnold, Enhao Gong, Srivathsa Pasumarthi
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
By Alzahra Altalib, Chunhui Li, Alessandro Perelli
arXiv:2606. 04033v1 Announce Type: new Abstract: The validation of advanced nuclear reactor designs and fuel concepts requires critical experiments with high neutronic similarity to the target technology.
By Will Savage, Logan Burnett, Dean Price
arXiv:2609.39010v1 Announce Type: cross
Abstract: Objective: Head and neck (HN) proton therapy spans six to seven weeks of anatomical change, while offline replanning takes about a week. We present a...
By Yizhou Wu, Ryan J. Sanford, Huiqiao Xie, Jie Ding, Shupeng Chen, Tung-Ho Wu, Ping-Hsiu Wu, Justin Roper, Jun Zhou, Minglei Kang, Bill Stokes, Sibo Tian, David S. Yu, Xiaofeng Yang, Chih-Wei Chang
arXiv:2607. 11941v1 Announce Type: cross Abstract: Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality.
By Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor, Kah Ong Michael Goh, S. M. Hasan Mahmud, Md Mahfuzur Rahman
arXiv:2608. 12795v1 Announce Type: cross Abstract: Simulating the ALICE Zero Degree Calorimeter (ZDC) neutron detector responses at the LHC is computationally expensive, requiring complex Monte Carlo chains.
By Emilia Majerz, Jacek Otwinowski, Witold Dzwinel, Jacek Kitowski