MRI Recovery with a Self-Calibrated Denoiser
Sizhuo Liu, Philip Schniter, Rizwan Ahmad
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.
BibTeX
@inproceedings{icassp2022_mrirecoverywitha,
title = {MRI Recovery with a Self-Calibrated Denoiser},
author = {Sizhuo Liu and Philip Schniter and Rizwan Ahmad},
booktitle = {ICASSP 2022},
year = {2022}
}