Meeting Banner
Abstract #0844

Deep Plug-and-Play multi-coil compressed sensing MRI with matched aliasing: the Denoising-P-VDAMP algorithm

Charles Millard1, Aaron Hess1, Boris Mailhe2, and Jared Tanner3
1Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 2Siemens, Princeton, NJ, United States, 3Mathematical Institute, University of Oxford, Oxford, United Kingdom


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.

This abstract and the presentation materials are available to members only; a login is required.

Join Here