Expectation Consistent Plug-and-Play for MRI

• Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Vol. 2022; pp. 8667 - 8671
• Main Authors: Shastri, Saurav K., Ahmad, Rizwan, Metzler, Christopher A., Schniter, Philip
• Format: Conference Proceeding Journal Article
• Language: English
• Published: United States IEEE 01.01.2022
• Subjects: Gaussian noise; Magnetic resonance imaging; Message passing; Neural networks; Prediction algorithms; Signal processing; Signal processing algorithms
• ISSN: 1520-6149; 2379-190X
• DOI: 10.1109/ICASSP43922.2022.9747424

For image recovery problems, plug-and-play (PnP) methods have been developed that replace the proximal step in an optimization algorithm with a call to an application-specific denoiser, often implemented using a deep neural network. Although such methods have been successful, they can be improved. For example, the denoiser is often trained using white Gaussian noise, while PnP's denoiser input error is often far from white and Gaussian, with statistics that are difficult to predict from iteration to iteration. PnP methods based on approximate message passing (AMP) are an exception, but only when the forward operator behaves like a large random matrix. In this work, we design a PnP method using the expectation consistent (EC) approximation algorithm, a generalization of AMP, that offers predictable error statistics at each iteration, from which a deep-net denoiser can be effectively trained.