arXiv:2606. 24313v1 Announce Type: new Abstract: AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging.
By Martin Valls (UFR SFA), Pascal Bourdon (UFR SFA), Christine Fernandez-Maloigne (LabCom I3M), Guillaume Herpe (CHU Poitiers -- Radio, DACTIM-MIS), David Helbert (UFR SFA)
arXiv:2505.05855v4 Announce Type: replace
Abstract: Multi-contrast super-resolution (MCSR) is crucial for enhancing MRI but current deep learning methods are limited. They typically require large, pa...
By Yinzhe Wu, Hongyu Rui, Fanwen Wang, Jiahao Huang, Zhenxuan Zhang, Haosen Zhang, Zi Wang, Guang Yang
arXiv:2603. 03710v3 Announce Type: replace-cross Abstract: Zero-shot MRI reconstruction relies on generative priors, but single-modality unconditional priors produce hallucinations under severe ill-posedness.
By Seunghoi Kim, Chen Jin, Henry F. J. Tregidgo, Matteo Figini, Daniel C. Alexander
arXiv:2605.24625v2 Announce Type: replace
Abstract: Ultra-low-field (ULF) MRI offers portable and accessible neuroimaging but suffers from reduced signal-to-noise ratio and limited spatial resolution...
By Toufiq Musah, Salvatore Calcagno, Federica Proietto Salanitri, Xiaomeng Li, Maruf Adewole, Marawan Elbatel
AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging. Multi-modal image analysis plays a crucial role in optimizing examination quality, yet acquiring multiple imaging modalities in clinical settings remains resource-intensive and time-consuming, especially for 3D imaging.
arXiv:2608.28787v1 Announce Type: new
Abstract: Joint-embedding predictive architectures (JEPAs) have primarily been developed for self-supervised representation learning. Denoising JEPA (D-JEPA) rec...
By Meng Zhou, Wenhao You, Yuxing Chen, Yueying Tian