Michael John Allison1, Jeffrey A. Fessler1
1Department of Electrical Engineering & Computer Science, the University of Michigan, Ann Arbor, MI, United States
Accurate coil sensitivity estimates are required to avoid artifacts in many parallel imaging techniques. Existing regularized methods provide sufficiently accurate estimates, but often at a high computational cost. We propose an iterative technique that uses Augmented Lagrangian principles to efficiently compute sensitivity estimates. Our method generated sensitivity estimates for a challenging breast phantom dataset in half the time required by a conjugate gradient algorithm. We therefore conclude that our AL approach provides an efficient strategy for accurately estimating coil sensitivities.