Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction

TL;DR

This paper introduces CS+M, a novel multi-task optimization framework that jointly reconstructs undersampled MRI images and estimates motion, significantly reducing artifacts and improving image quality across various clinical applications.

Contribution

The paper presents a unified optimization model for simultaneous MRI reconstruction and motion estimation, enhancing image quality in undersampled dynamic MRI scans.

Findings

Reduces blurring artifacts in reconstructed images

Preserves fine details and target shape

Achieves highest quality reconstructions at high undersampling rates

Abstract

We address the problem of reconstructing high quality images from undersampled MRI data. This is a challenging task due to the highly ill-posed nature of the problem. In particular, in dynamic MRI scans, the interaction between the target structure and the physical motion affects the acquired measurements leading to blurring artefacts and loss of fine details. In this work, we propose a framework for dynamic MRI reconstruction framed under a new multi-task optimisation model called Compressed Sensing Plus Motion (CS+M). Firstly, we propose a single optimisation problem that simultaneously computes the MRI reconstruction and the physical motion. Secondly, we show our model can be efficiently solved by breaking it up into two more computationally tractable problems. The potentials and generalisation capabilities of our approach are demonstrated in different clinical applications including cardiac cine, cardiac perfusion and brain perfusion imaging. We show, through numerical and graphical experiments, that the proposed scheme reduces blurring artefacts and preserves the target shape and fine details. We also report the highest quality reconstruction under highly undersampling rates in comparison to several state of the art techniques.