We present the Parallel Variable Density Approximate Message Passing (P-VDAMP) algorithm for compressed sensing MRI, which extends the recently proposed single-coil VDAMP algorithm to multiple coils. We evaluate the performance of P-VDAMP on eight datasets at a number of undersampling factors and find that it converges to a mean-squared error similar to the Fast Iterative Shrinkage Thresholding Algorithm (FISTA) with an optimally tuned sparse weighting, but in around 5x fewer iterations and without the need to tune model parameters.
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