  Purpose: To develop a synergistic image reconstruction framework that
exploits multicontrast (MC), multicoil, and compressed sensing (CS)
redundancies in magnetic resonance imaging (MRI).
  Approach: CS, MC acquisition, and parallel imaging (PI) 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. Inspired by total variation theory, we introduce an isotropic MC
image regularizer and attain its full potential by integrating it into
compressed MC multicoil MRI. A convex optimization problem is posed to model
the new variational framework and a first-order algorithm is developed to solve
the problem.
  Results: It turns out that the proposed isotropic regularizer outperforms
many of the state-of-the-art reconstruction methods not only in terms of
rotation-invariance preservation of symmetrical features, but also in
suppressing noise or streaking artifacts, which are normally encountered in PI
methods at aggressive undersampling rates. Moreover, the new framework
significantly prevents intercontrast leakage of contrast-specific details,
which seems to be a difficult situation to handle for some variational and
low-rank MC reconstruction approaches.
  Conclusions: The new framework is a viable option for image reconstruction in
fast protocols of MC parallel MRI, potentially reducing patient discomfort in
otherwise long and time-consuming scans.
