Researchers: Angelica Aviles-Rivero, Martin Graves, Carola-Bibiane Schönlieb, Guy Williams
The advent of Compressed Sensing (CS) in Magnetic Resonance Imaging (MRI) has allowed tackling the constraints associated with slow data acquisition, which is still considered a challenge in clinical practice. Current research in MRI is based on using CS implications to reconstruct high-quality images from a subset of k-space data acquired in an incoherent manner. In this project, we aim to address the question – How to achieve a high-quality MR Image Reconstruction to offer a better clinical understanding? We cope with this question by introducing a mathematical framework for improving undersampled MRI data reconstruction, which we call CS+M, where M stands for motion. The significance here, and unlike existing solutions is that by modeling explicitly and simultaneously the inherent complex motion patterns, given by physiological or involuntary motion, in a CS setting, synergies in a complex variational problem are created. These synergies have positive clinical potentials in terms of improving image quality while reducing motion artifacts. We exhaustively tested our approach on realistic datasets and we reported that our approach resulted on having reconstructed images with the highest quality w.r.t CS and being closest to the gold-standard reconstruction.