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Abstract #4117

CS+M: A Simultaneous Reconstruction and Motion Estimation Approach for Improving Undersampled MRI Reconstruction.

Angelica I. Aviles-Rivero1, Guy B. Williams2, Martin J. Graves3, and Carola-Bibiane Schönlieb4

1Department of Pure Mathematics and Mathematical Statistics (DPMMS), University of Cambridge, Cambridge, United Kingdom, 2Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom, 3Department of Radiology, University of Cambridge, Cambridge, United Kingdom, 4Department of Applied Mathematics and Theoretical Physics (DAMTP), University of Cambridge, Cambridge, United Kingdom

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 work, we introduce 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 clinical potentials in terms of improving image quality while reducing motion artifacts.

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