Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction

TL;DR

This paper introduces a joint edge optimization deep unfolding network that leverages edge priors and co-regularization to significantly improve MRI reconstruction quality, reducing scan times.

Contribution

It proposes a novel joint edge optimization model integrated into a deep unfolding network, enhancing MRI reconstruction by effectively utilizing edge information and correlations.

Findings

Outperforms existing MRI reconstruction methods across various datasets and sampling schemes.

Effectively utilizes edge priors to improve image quality.

Demonstrates robustness across multi-coil and single-coil MRI data.

Abstract

Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement. In this paper, we build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them. Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process. Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information.Numerical experiments, consisting of multi-coil and single-coil MRI data with different sampling schemes at a variety of sampling factors, demonstrate that the proposed method outperforms other compared methods.