A multi-channel framework for joint reconstruction of multi-contrast parallel MRI

Compressed sensing, multi-contrast and parallel imaging have been individually well developed in recent literature but the combination of the three has not been equally well studied, much less the potential benefits of isotropy within such a setting. In this paper, a novel isotropic image regularizer is introduced to help develop a synergistic image reconstruction framework that exploits multi-contrast, multi-coil and compressed sensing redundancies in MRI. A convex optimization problem is introduced to model the new framework and a first-order algorithm is developed to solve the problem. Compared to other state-of-the-art methods, image quality is significantly improved thanks to guaranteed isotropy and retention of contrast-specific features without leakage to other contrasts. The new method turns out to be a robust and viable option for clinical protocols of fast multi-contrast parallel MRI, reducing scan times and patient discomfort.