Compressed Sensing Plus Motion (CS+M): A New Perspective for Improving Undersampled MR Image Reconstruction
This addresses the challenge of motion-induced artifacts in dynamic MRI for clinical applications like cardiac and brain imaging, representing a novel method for a known bottleneck.
The paper tackles the problem of reconstructing high-quality images from undersampled dynamic MRI data by proposing the Compressed Sensing Plus Motion (CS+M) framework, which simultaneously computes reconstruction and physical motion, resulting in reduced blurring artifacts and the highest quality reconstruction under highly undersampling rates compared to state-of-the-art techniques.
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.