arXiv Computer Vision By Felix Frederik Zimmermann, Patrick Schuenke, Christoph S. Aigner, Bill A. Bernhardt, Mara Guastini, Johannes Hammacher, Noah Jaitner, Andreas Kofler, Leonid Lunin, Stefan Martin, Catarina Redshaw Kranich, Jakob Schattenfroh, David Schote, Yanglei Wu, Christoph Kolbitsch

MRpro: open framework for model-based, learned, and quantitative MR imaging

Read the original on arXiv Computer Vision →

The Flow has not summarised this story yet — read it at arXiv Computer Vision.

arXiv AI
Aug 24

Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising

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
arXiv Machine Learning
Aug 27

FlowMoDL: Model-Based Deep Learning with Conjugate-Gradient Data Consistency for Highly Accelerated 4D Flow MRI Reconstruction

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 AI
Aug 19

Harnessing Magnitude-Only and Complex Measurements for Improved Dynamic MRI Reconstruction with Learned Priors

The paper introduces τ+C+Mag, a physics‑driven deep learning method that incorporates auxiliary k‑space magnitude information into accelerated steady‑state dynamic MRI reconstruction. By observing strong consistency of k‑space magnitudes across time‑frames, the authors develop an ADMM‑based unrolling framework with a magnitude‑aware data‑fidelity term, using quadratically smoothed optimization and momentum updates to handle non‑differentiability and non‑convexity. Experiments on retrospectively and prospectively undersampled cine and phase‑contrast flow MRI datasets show improved artifact suppression, sharper anatomical detail, and better phase preservation compared to conventional PD‑DL approaches, as confirmed by blinded expert readers.

By Mahdi Saberi, Ya\c{s}ar Utku Al\c{c}alar, Merve G\"{u}lle, Chetan Shenoy, Mehmet Ak\c{c}akaya
arXiv Computer Vision
Sep 3

Data-Efficient Networks for Multi-Contrast MRI Reconstruction based on a Generalized Content/Style Prior

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 AI
Aug 18

CG-GLORE: A Conjugate Gradient-Based Global-Local Regularization Network for Sparse-View CT Reconstruction

arXiv:2608. 15246v1 Announce Type: cross Abstract: Sparse-view computed tomography (CT) reduces radiation dose by acquiring fewer projection views, but the resulting inverse problem is highly ill-posed and often produces severe streak artifacts.

By Tran Xuan Hieu Le, Doanh C. Bui, Vu Trung Duong Le, Hoai Luan Pham, Khang Nguyen, Mai K. Nguyen, Tu Bao Ho, Yasuhiko Nakashima