Model-based free-breathing cardiac MRI reconstruction using deep learned \& STORM priors: MoDL-STORM

TL;DR

This paper presents a novel model-based framework combining deep learned and STORM priors to improve free-breathing, ungated cardiac MRI reconstruction from highly undersampled data, leveraging local and non-local image correlations.

Contribution

It introduces a new formulation integrating deep learning with prior information for improved cardiac MRI reconstruction from undersampled data.

Findings

Demonstrates potential for accelerating free-breathing cardiac MRI

Effectively exploits local and non-local image correlations

Shows preliminary success in reconstructing undersampled data

Abstract

We introduce a model-based reconstruction framework with deep learned (DL) and smoothness regularization on manifolds (STORM) priors to recover free breathing and ungated (FBU) cardiac MRI from highly undersampled measurements. The DL priors enable us to exploit the local correlations, while the STORM prior enables us to make use of the extensive non-local similarities that are subject dependent. We introduce a novel model-based formulation that allows the seamless integration of deep learning methods with available prior information, which current deep learning algorithms are not capable of. The experimental results demonstrate the preliminary potential of this work in accelerating FBU cardiac MRI.