arXiv:2603.23295v2 Announce Type: replace
Abstract: Radiotherapy workflows for oncological patients increasingly rely on multi-modal medical imaging, commonly involving both Magnetic Resonance Imagin...
By Konstantinos Barmpounakis, Theodoros P. Vagenas, Maria Vakalopoulou, George K. Matsopoulos
arXiv:2605.20470v2 Announce Type: replace-cross
Abstract: Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noi...
By Alzahra Altalib, Chunhui Li, Haytham Ahmad Alewaidat, Khaled Z. Alawneh, Ahmad Awad Qandeel, Alessandro Perelli
arXiv:2608. 03430v1 Announce Type: cross Abstract: Four-dimensional cone beam CT (4D CBCT) is important for image-guided radiation therapy of thoracic cancers, but its use is limited by long scan times, causing high patient dose and motion/sparse-sampling artifacts.
By Ivo Herzig, Pascal Paysan, Daniel Barco, Marc Andr\'e Stadelmann, Frank-Peter Schilling, Igor Peterlik, Michal Walczak, Lijin Aryananda, Woo Sang Ahn, Rudolf Marcel F\"uchslin, Lukas Lichtensteiger
arXiv:2608.28705v1 Announce Type: cross
Abstract: Dose accumulation is increasingly important in adaptive radiation therapy and reirradiation, but its clinical validity depends on the performance of...
By Hengjie Liu, Manju Sharma, Xinyi Fu, Di Xu, Ke Sheng
The paper introduces a one‑pass conditional 3D rectified flow (3D Flow) framework for denoising whole‑body PET images, employing an optimized non‑uniform sampling strategy and a linear‑interpolant velocity‑matching objective. It reconstructs a full 3D volume in about 30 seconds, dramatically faster than multi‑hour 3D diffusion models, while maintaining high global image quality and lesion conspicuity even at ultra‑low doses (down to 1/100 of standard). Zero‑shot transfer tests on independent clinical data demonstrate robust performance across datasets and unseen dose levels.
By Jiale Shen, Guolin Wang, Chenhao Wang, Xinhui Su, Wei Luo, Feng Yu
Magnetic resonance imaging (MRI) super-resolution is vital for improving diagnostic accessibility, yet most methods treat it as a deterministic mapping from a fixed low-resolution input to a high-resolution target. This overlooks a key property of MRI acquisition physics: spatial resolution and signal-to-noise ratio (SNR) are inherently coupled, making any given low-resolution scan merely one of many possible realizations under varying acquisition trade-offs.
arXiv:2606. 07658v1 Announce Type: cross Abstract: Maximal safe resection is the primary objective in glioma surgery.
By Santiago Cepeda, Olga Esteban-Sinovas, Ignacio Arrese, Rosario Sarabia
arXiv:2608.29348v1 Announce Type: new
Abstract: Background: Patient details and acquisition metadata are important for clinical decisions, image quality control, and automated research pipelines, but...
By Jakob Wasserthal, Joshy Cyriac, Michael Bach, Kimia Mozahheb Yousefi, Minh-Son To, M\'at\'e Sik, C\'edric H\'emon, Thomas Weikert, Martin Segeroth
The paper introduces SWIFT, a Swin V2‑based model pretrained on 10,444 3D CT volumes and fine‑tuned for rectal cancer segmentation on T2‑weighted MRI. Four configurations—full fine‑tuning (SWIFT), decoder compression (SWIFTe), low‑rank adaptation (SWIFTe‑LoRA), and a LoRA‑decoder ensemble (SWIFTe‑LDE4)—were evaluated on 247 cases, showing that SWIFTe reduces parameters by 70.1% while improving tumor detection and radiomic agreement. The study also demonstrates a trade‑off between detection and boundary agreement, and highlights that SWIFTe‑LDE4 achieves the lowest calibration errors after temperature scaling.
By Aneesh Rangnekar, Jorge Tapias Gomez, Joseph O Deasy, Harini Veeraraghavan
The study introduces a voxel-spacing-aware radiomic framework that incorporates physical geometry into texture computation without resampling, modifying PyRadiomics to preserve native image signals. Four extraction configurations—native non-resampled (NR), isotropic resampling (RS), voxel-spacing-aware (VS), and fake-isotropic preprocessing (FK)—were compared across 685 CT pulmonary nodules and 209 MRI breast cases, evaluating 196 radiomic descriptors. Results show that VS closely matches NR (median ICC(A,1) ≈0.998) while RS and FK exhibit lower agreement, indicating that spacing metadata alone can significantly influence radiomic features.
By David Corral Fontecha, Juan Miranda Bautista, Pablo Menendez Fern\'andez-Miranda, Sergio Rubio-Mart\'in, Lara Lloret Iglesias, Jose A. Vega
arXiv:2602. 05387v2 Announce Type: replace-cross Abstract: MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation.
By Zolnamar Dorjsembe, Hung-Yi Chen, Furen Xiao, Hsing-Kuo Pao
arXiv:2609.24468v2 Announce Type: replace
Abstract: Three-dimensional (3D) multi-contrast magnetic resonance imaging (MCMRI) provides rich anatomical and quantitative information but requires long ac...
By Jingran Xu, Dong Liang, Hairong Zheng, Yuanyuan Liu, Yanjie Zhu