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With the advent of multi-coil imaging and compressed sensing, a number ofmodel based reconstruction algorithms have been created. They incorporate amultitude of different regularization functions based on physics, observedphenomenology, and heuristics. Moreover, several iterative methods exist thatattempt to simultaneously estimate the sensitivity maps and the image. In thismanuscript, we present a generalization of several existing iterative modelbased algorithms. We devise a calibrationless instance of this generalizationthat only incorporates regularization terms based on physics and the acceptedcompressed sensing phenomenology of sparsity in the wavelet domain. We comparethe results of the new amalgamated optimization problem with existing methodson both simulated and real datasets. We show that the images reconstructedusing the new method, entitled Multi-coil Compressed Sensing (MCCS), are ofhigher quality than existing methods in all cases studied.

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