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
The paper introduces AURA, an nnU-Net-based asymmetric supervision strategy for segmenting ultra‑low‑field (0.064 T) pediatric brain MRI. It treats high‑field‑derived (HF) and low‑field‑edited (LF) annotations as distinct observations, anchoring training to the HF mask while gating LF contributions through a reliability mechanism. On a 16‑case development split, AURA achieved Dice scores comparable to HF‑only training and improved boundary metrics, demonstrating its potential for ULF MRI segmentation.
By Ha-Hieu Pham, Dang P. M. Cao, Minh Hoang Pham, Khanh Nguyen Vo Ngoc, Thanh-Huy Nguyen, Ulas Bagci, Huy-Hieu Pham
arXiv:2508.11450v2 Announce Type: replace-cross
Abstract: Subcortical segmentation in neuroimages plays an important role in understanding brain anatomy and facilitating computer-aided diagnosis of t...
By Augustine X. W. Lee, Pak-Hei Yeung, Jagath C. Rajapakse
NeuroTS-Net is a 3‑D encoder‑decoder CNN designed for multi‑class semantic segmentation of pediatric brain tumors in multi‑modal MRI. It uses a dual‑scale raw‑detail stream, adaptive low‑resolution context selection, and detail‑preserving multipath downsampling to maintain fine intensity and boundary information while modeling broader tumor context. Trained on the BraTS 2026 pediatric dataset, it outperformed nnU‑Net and MedNeXt, achieving Dice scores of 0.938/0.937 on internal validation and 0.927/0.926 on the official challenge set.
By Darius Peteleaza, Razvan-Gabriel Dumitru, Bogdan Neamtu, Arpad Gellert, Mariana Sandu, Claudiu Matei
arXiv:2606. 15457v1 Announce Type: cross Abstract: 3D FLAIR MRI is widely recommended as one of the standard MRI sequences for brain imaging in multiple sclerosis (MS), but publicly available MS datasets remain relatively small and vary across scanners, acquisition protocols, and lesion patterns.
By Weidong Zhang, Yongchan Jung, Shafayat Mowla Anik, Furen Xiao, Vasudevan Janarthanan, Enkhzaya Chuluunbaatar, Byeong Kil Lee, Jeeho Ryoo
The paper introduces a 3D residual wavelet diffusion model for super‑resolving ultra low‑field MRI scans. By using a lossless wavelet reparameterisation, residual shifting, and domain randomisation, the method fits whole‑brain data on a single GPU, speeds up sampling, and generalises across scanners. It achieves volumetric accuracy comparable to leading regression approaches while producing per‑voxel uncertainty maps that reveal under‑determined regions and preserves disease‑relevant atrophy in cognitively impaired subjects.
By Rui W. Yeow, Millie Beament, Fred Dick, Raha Razin, Martina Bocchetta, David L. Thomas, Henry F. J. Tregidgo, Daniel C. Alexander, James H. Cole