Work

MRI Recovery with a Self-Calibrated Denoiser

Year: 2022

Type: conference-paper

Abstract: Plug-and-play (PnP) methods that employ application-specific denoisers have been proposed to solve inverse problems, including MRI reconstruction. However, training application-specific denoisers is not feasible for many applications due to the lack of training data. In this work, we propose a PnP-inspired recovery method that does not require data beyond the single, incomplete set of measurements. The proposed self-supervised method, called recovery with a self-calibrated denoiser (ReSiDe), trains the denoiser from the patches of the image being recovered. The denoiser training and a call to the denoising subroutine are performed in each iteration of a PnP algorithm, leading to a progressive refinement of the reconstructed image. For validation, we compare ReSiDe with a compressed sensing-based method and a PnP method with BM3D denoising using single-coil MRI brain data.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp43922.2022.9746785

Authors: Sizhuo Liu, Philip Schniter, Rizwan Ahmad

Institution: The Ohio State University

Language: English

Topic: Image and Signal Denoising Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Heart, Lung, and Blood Institute, National Institute of Biomedical Imaging and Bioengineering, HORIZON EUROPE Health

Awards: G488930597, A comprehensive deep learning framework for MRI reconstruction