FlowMoDL is an unrolled neural network designed for highly accelerated 4D flow MRI reconstruction, optimizing both anatomical magnitude and phase-derived velocity accuracy. It alternates a learned (3+1)D spatiotemporal denoiser with conjugate‑gradient data‑consistency updates, using a dual‑pathway conditioning scheme to handle acceleration factors from 10× to 50×. Trained with a deep‑supervision composite loss that penalizes velocity magnitude and angular errors, FlowMoDL outperforms classical and deep‑learning baselines on the multi‑center CMRx4DFlow dataset, achieving superior gradient‑step efficiency and robust convergence across all acceleration factors.
By Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter
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:2609.00960v1 Announce Type: new
Abstract: Magnetic resonance images of the same subject look markedly different across field strengths, which complicates the comparison and pooling of data acro...
By Baris Imre, Aram Salehi, Levente Baljer, Andrew Webb, Marius Staring, Efe Ilicak
The paper introduces CM-RED, a fast MRI reconstruction method that combines a pretrained consistency model with the regularization by denoising framework. By integrating controlled noise injection into accelerated proximal gradient updates, CM-RED achieves high‑quality reconstructions on fastMRI knee and brain datasets with only four network function evaluations. It consistently outperforms existing diffusion‑ and consistency‑based approaches in quantitative metrics, visual fidelity, and robustness to hyperparameter changes.
By Merve G\"ulle, Junno Yun, Ya\c{s}ar Utku Al\c{c}alar, Mehmet Ak\c{c}akaya
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
arXiv:2602. 21987v3 Announce Type: replace-cross Abstract: Low-dose CT images are essential for reducing radiation exposure in cancer screening, pediatric imaging, and longitudinal monitoring protocols, but their quality is often degraded by noise from low-dose acquisition, patient motion, or scanner limitations, affecting both clinical interpretation and downstream analysis.
By Jitindra Fartiyal, Pedro Freire, Sergei K. Turitsyn, Sergei G. Solovski
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:2610.00279v1 Announce Type: new
Abstract: The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease p...
By Eirini Cholopoulou, Dimitrios E. Diamantis, Dimitris K. Iakovidis
The paper explores large‑scale pretraining to enhance deep learning‑based geometric distortion correction for diffusion‑weighted imaging (DWI). By framing the task as image reconstruction, the authors compare a non‑pretrained baseline with self‑supervised and generative pretrained models, finding that the cWDM model yields the best quantitative and qualitative results. When applied to low‑resource, high‑throughput settings in a low‑ and middle‑income country, the pretrained models faced transferability issues, but aligning images to a common standard space improved predictions, indicating that harmonized preprocessing can aid cross‑domain deployment.
By Saroj Khanal, Yashawant Kumar Yadav, Kritam Bhattarai, Jeevan Neupane, Shristi Subedi, Saship Gwachha, Manish Kumar Tiwari, Dong Zhang, Confidence Raymond, Aondona Moses Iorumbur, Udunna Anazodo, Surendra Maharjan, Bishesh Khanal, Mahesh Shakya, Pralhad Kumar Shrestha
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
FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction proposes a new training method for unrolled neural networks used in MRI reconstruction. By interpreting unrolled networks as discretizations of conditional probability flows, the authors derive cascade parameters from Flow Matching and align intermediate reconstructions with the ideal Flow Matching trajectory. Experiments on three MRI datasets demonstrate that FLAT stabilizes the reconstruction trajectory across sub-networks and improves the final reconstruction quality.
By Kehan Qi, Saumya Gupta, Xiaoling Hu, Qingqiao Hu, Weimin Lyu, Yicun Wang, Chao Chen
arXiv:2609.39083v1 Announce Type: new
Abstract: Super-resolution and quality enhancement of 1.5\,T brain MRI are normally validated with image-fidelity metrics, although their purpose is to improve d...
By Kavitha Viswanathan, Harsh Choudhary, Amit Sethi