With the advent of multi-coil imaging and compressed sensing, a number of model based reconstruction algorithms have been created. They incorporate a multitude of different regularization functions based on physics, observed phenomenology, and heuristics. Moreover, several iterative methods exist that attempt to simultaneously estimate the sensitivity maps and the image. In this manuscript, we present a generalization of several existing iterative model based algorithms. We devise a calibrationless instance of this generalization that only incorporates regularization terms based on physics and the accepted compressed sensing phenomenology of sparsity in the wavelet domain. We compare the results of the new amalgamated optimization problem with existing methods on both simulated and real datasets. We show that the images reconstructed using the new method, entitled Multi-coil Compressed Sensing (MCCS), are of higher quality than existing methods in all cases studied.
@article{arxiv.2007.00165,
title = {Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization},
author = {Nicholas Dwork and Ethan M. I. Johnson and Daniel O'Connor and Jeremy W. Gordon and Adam B. Kerr and Corey A. Baron and John M. Pauly and Peder E. Z. Larson},
journal= {arXiv preprint arXiv:2007.00165},
year = {2023}
}