← Back to all news
arXiv Machine Learning By Tristan Gottwald, Michelle Bruch, Mubashir-Ul Hassan, Fatma Alickovic, Milan Kloiber, Daniel Tenbrinck, Torsten Panholzer, Melanie Schaller, Jana Hutter

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

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

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
diffusion
More like this →
arXiv Computer Vision
4d ago

FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction

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
diffusion
More like this →