The study introduces ART‑Net, an anatomy‑aware residual attention network designed to refine four‑fold accelerated SENSE brain MRI. In a prospective paired study of 80 participants, ART‑Net achieved the highest peak signal‑to‑noise ratio and structural similarity index among evaluated methods, and it preserved anatomical fidelity with superior Dice coefficients for medial temporal and whole‑brain structures. Radiologist assessments also indicated improved gradient fidelity, regional contrast, and overall structural quality.
By Changjing Chai, Bin Huang, Libo Xu, Jian Zhou, Boyang Pan, Kristen W Yeom, Qiyong Gong, Nan-Jie Gong
The paper introduces AGW-PBR, a brain MRI super‑resolution method that emphasizes tissue‑transition regions affected by the partial‑volume effect. It uses a low‑resolution backbone guided by Sobel edges, soft latent‑basis assignment, and grid‑anchored warping, while training‑time weights are derived from tissue‑mixture entropy computed from registered T1/T2/PD images. Experiments on IXI T2‑weighted scans at 2×, 4×, and 6× show improved full‑image reconstruction and regional fidelity, and the backbone also performs well on fastMRI data without PVE supervision.
By Xiao Tong, Wenyun Yang, Ziheng Zhang, Jingzhi Han, Zhaochu Luo, Jinbo Yang
DGCM-SSFA is a compact multi‑domain attention framework that synthesizes high‑fidelity 7T MRI from routine 3T scans using only 11 million parameters. It combines a Dual‑Granularity Cross‑Modulated Feature Modulator (DGCM) and a Sparse Strip Frequency Attention (SSFA) module within a U‑Net Transformer backbone to enhance local edge details and capture global spatial context efficiently. Experiments on UNC and BNU 3T‑to‑7T datasets show competitive structural fidelity, achieving SSIM scores of 0.8431 (BNU T2w) and 0.8100 (BNU T1w) while remaining far smaller than Transformer‑based alternatives.
By Zhichun Fan, Zhiqiang Chen, Lanling Liu, Lingjun Liu, Zhonghua Xie
SRUG is a supervised, fusion‑driven generator network designed for paired MRI sequence synthesis. It eliminates the generator‑discriminator competition by directly learning a deterministic source‑to‑target mapping, using a residual encoder, full‑resolution stem, and a nested multi‑scale decoder to preserve structural fidelity. Experiments on BraTS 2023 and IXI demonstrate competitive reconstruction quality and cross‑dataset transferability, supporting direct supervised generation as a practical approach for medical image translation.
By Xihe Qiu, Yang Dai, Xiaoyu Tan, Sijia Li, Fenghao Sun, Lu Gan, Liang Liu
arXiv:2605.05522v3 Announce Type: replace-cross
Abstract: Although self-supervised pretraining is expected to learn broadly transferable representations, its effectiveness across imaging modalities s...
By Aneesh Rangnekar, Joao Miranda, Natally Horvat, Stephanie Chahwan, Samir Alrayess, Aditya Apte, Aditi Iyer, Eve LoCastro, Revathi Ravella, Marc J Gollub, Iva Petkovska, Jesse Joshua Smith, Paul Romesser, Julio Garcia-Aguilar, Harini Veeraraghavan, Joseph O Deasy
Rhamba is a region‑aware pretraining framework for resting‑state fMRI that combines anatomically guided masking with hybrid Attention‑Mamba architectures. The study pretrained models on the ABIDE dataset using three masking strategies (Any, Majority, Pure) and evaluated four architectural variants, finding that the Mamba‑Attention (MA) hybrid achieved the best average AUROC on downstream schizophrenia and ADHD classification tasks. Explainable AI via Integrated Gradients highlighted that performance depends on the interaction between masking strategy and architecture rather than a single dominant configuration.
By Pankaj Pandey, Ruthwik Reddy Doodipala, Pratheek Eranki, Carolina Torres-Rojas, Manob Jyoti Saikia, Ranganatha Sitaram
The paper introduces CoSMo-RecNet, a modular framework for multi-contrast MRI reconstruction that operates effectively in low-data regimes. It leverages a reusable content/style prior learned from large, unpaired multi-contrast image datasets, allowing a lightweight unrolled network to refine reconstructions using only a few task‑specific training samples. Experiments on low‑field 0.3 T and ultra‑low‑field 47 mT datasets demonstrate that CoSMo-RecNet outperforms parameter‑matched MoDL, classical reconstruction, transfer learning, and zero‑shot methods, achieving higher quality with as few as five training subjects.
By Chinmay Rao, Efe Il{\i}cak, Matthias J. P. van Osch, Mariya Doneva, Laurens Beljaards, Navid Jabarimani, Nicola Pezzotti, Marius Staring
The paper introduces MR‑DiffuSR, a 3‑D latent diffusion framework that uses high‑resolution T1w structural priors to guide super‑resolution of thick‑slice FLAIR MRI scans. By applying cross‑modality structural swin attention and a mixed‑scale degradation strategy, the method avoids hallucinations and remains robust across varying slice thicknesses. On ADNI datasets, MR‑DiffuSR outperforms CNN and 2‑D diffusion baselines, achieving high PSNR, SSIM, and low LPIPS, and maintains strong white‑matter hyperintensity segmentation performance even at 7 mm equivalent slice thickness.
By Haoyu Lan, Jiazhen Zhang, John Onofrey, Bino Varghese, Nasim Sheikh-Bahaei, Arthur W. Toga, Jeiran Choupan
SliceBridge is a new framework for repairing corrupted slice intervals in T1‑weighted MRI by using rectified flow matching conditioned on surrounding intact slices and their relative positions. It enforces through‑plane consistency by coupling slices within the interval through shared noise, flow time, and synchronized sampling, then reinserts the restored interval without altering other slices. In experiments on 9,877 brain MRIs and 581 external subjects, SliceBridge reduced slice‑to‑slice error by 32.9%–41.3% and improved SSIM across interval lengths, while lowering downstream segmentation volume estimation error from 1.95% to 1.05%.
By Jiheng Li, Michael E. Kim, Trent Schwartz, Gaurav Rudravaram, Derek B. Archer, Timothy J. Hohman, the Alzheimer's Disease Neuroimaging Initiative, Lianrui Zuo, Bennett A. Landman
arXiv:2602.00348v2 Announce Type: replace
Abstract: Metal implants in MRI cause severe artifacts that degrade image quality and hinder clinical diagnosis. Traditional approaches address metal artifac...
By Zhengyi Lu, Ming Lu, Chongyu Qu, Junchao Zhu, Junlin Guo, Marilyn Lionts, Yanfan Zhu, Yuechen Yang, Tianyuan Yao, Jayasai Rajagopal, Bennett Allan Landman, Xiao Wang, Xinqiang Yan, Yuankai Huo
arXiv:2607. 03568v1 Announce Type: cross Abstract: Objective: Accurate identification of acute ischemic infarcts on diffusion-weighted magnetic resonance imaging (DWI) is a critical prerequisite for reliable lesion quantification and effective clinical decision support in the management of cerebrovascular events.
By Hasan Ulutas, Muhammet Emin Sahin, Mustafa Fatih Erkoc, Esra Yuce, Turker Tuncer, Sengul Dogan, Serkan Kiranyaz
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