Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Image reconstruction methods based on structured low-rank matrix completion have drawn growing interest in magnetic resonance imaging. In this work, we propose a locally structured low-rank image reconstruction method which imposes low-rank constraints on submatrices of the Hankel structured k-space data matrix. Simulation experiments based on numerical phantoms and experimental data demonstrated that the proposed method achieves robust and significant improvements over the conventional, global structured low-rank methods across a variety of structured matrix constructions, sampling patterns and noise levels, at the cost of slower convergence speed only.

Original publication

DOI

10.1109/ISBI52829.2022.9761692

Type

Conference paper

Publication Date

01/01/2022

Volume

2022-March