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Abstract #1157

Near-optimal tuning-free multicoil compressed sensing MRI with Parallel Variable Density Approximate Message Passing

Charles Millard1,2, Aaron T Hess2, Jared Tanner1, and Boris Mailhe3
1Mathematical Institute, University of Oxford, Oxford, United Kingdom, 2Oxford Centre for Clinical Magnetic Resonance Research, University of Oxford, Oxford, United Kingdom, 3Digital Technology and Innovation, Siemens Healthineers, Princeton, NJ, United States

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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