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
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
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:2609.38644v1 Announce Type: cross
Abstract: 4D flow MRI measures time-resolved, three-directional blood velocity but requires long acquisition times, and its diagnostic signal is carried by the...
By Mengyuan Xue, Bochun Mei
arXiv:2603. 04438v3 Announce Type: replace-cross Abstract: Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction.
By Qingyong Zhu, Yumin Tan, Xiang Gu, Dong Liang
The paper introduces FLEX‑MRI, a method that accelerates MRI reconstruction by partitioning the recovery process into a few finite intervals defined by progressive k‑space support changes. Instead of learning fine‑grained local dynamics, FLEX‑MRI directly learns the reconstruction change across each interval and employs stochastic exploration during inference to sample alternative transition paths. Experiments on multi‑coil MRI datasets demonstrate that FLEX‑MRI outperforms competing methods while keeping computational efficiency comparable to few‑step baselines.
By Muhammad Usama Mirza, Esin \"Ozt\"urk I\c{s}{\i}k, Tolga \c{C}ukur
The paper introduces Shared LoRA, a parameter‑efficient approach for accelerated MRI reconstruction that uses a single set of LoRA adapters and a lightweight gating network to handle multiple acceleration factors. By freezing a pretrained SHFormer backbone and training the adapters on randomly sampled acceleration factors, the method learns reconstruction knowledge across factors. Experiments demonstrate that Shared LoRA achieves competitive PSNR and SSIM while using only about 5.3% of the total model parameters, and it generalizes well to unseen neighboring factors.
By Zhiwei Zhao, Weikang Gong, Zhongnian Li, Xinzheng Xu
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
arXiv:2606. 00100v1 Announce Type: cross Abstract: Self-supervised deep learning-based methods have shown great promise for accelerated magnetic resonance imaging (MRI) reconstruction, achieving high image quality without requiring fully sampled data for training.
By Tongxi Song, Ziyu Li, Zihan Li, Wen Zhong, Congyu Liao, Yang Yang, Hua Guo, Wenchuan Wu, Qiyuan Tian
arXiv:2610.08109v1 Announce Type: new
Abstract: Cardiac magnetic resonance imaging reconstruction aims to recover high-quality images from undersampled acquisitions, enabling faster scans while prese...
By Anam Hashmi, Mayug Maniparambil, Julia Dietlmeier, Kathleen M. Curran, Noel E. O'Connor
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
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