We present the Denoising Parallel Variable Density Approximate Message Passing (D-P-VDAMP) algorithm for multi-coil compressed sensing MRI with a learned prior. To our knowledge, D-P-VDAMP is the first Plug-and-Play method for multi-coil k-space data where the distribution of the training data's aliasing matches the actual distribution seen during reconstruction. We evaluate the performance of the proposed method on the fastMRI knee dataset and find substantial improvements in reconstruction quality compared with Plug-and-Play FISTA with the same network architecture in similar training and reconstruction time.
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