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

Objective: To develop a synergistic image reconstruction framework that exploits multi-contrast, multi-coil and compressed sensing redundancies in MRI. Methods: A novel isotropic multi-channel image regularizer is introduced, a convex optimization problem is posed to model the new framework and a first-order algorithm is developed to solve the problem. Results: Compared to other well-known state-of-the-art methods, image quality is significantly improved and contrast-specific details are preserved without leakage to other contrasts. Conclusion: The new method is a robust and viable option for clinical protocols of fast multi-contrast parallel MRI. Significance: Compressed sensing, multi-contrast and parallel imaging have been individually well developed 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 multi-channel image regularizer is introduced and its full potential is attained by its integration into compressed multi-contrast multi-coil MRI.