Multi-coil Magnetic Resonance Imaging with Compressed Sensing Using Physically Motivated Regularization

TL;DR

This paper introduces MCCS, a novel multi-coil MRI reconstruction method that leverages physics-based and sparsity regularizations, outperforming existing algorithms on simulated and real data.

Contribution

It generalizes existing iterative algorithms and presents a calibrationless approach using physics and sparsity regularizations for improved image quality.

Findings

MCCS produces higher quality images than existing methods.

The method performs well on both simulated and real datasets.

It effectively combines physics-based and sparsity regularizations.

Abstract

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.