Manifold recovery using kernel low-rank regularization: application to dynamic imaging.

ABSTRACT: We introduce a novel kernel low-rank algorithm to recover free-breathing and ungated dynamic MRI data from highly undersampled measurements. The image frames in the free breathing and ungated dataset are assumed to be points on a bandlimited manifold. We show that the non-linear features of these images satisfy annihilation conditions, which implies that the kernel matrix derived from the dataset is low-rank. We penalize the nuclear norm of the feature matrix to recover the images from highly undersampled measurements. The regularized optimization problem is solved using an iterative reweighted least squares (IRLS) algorithm, which alternates between the update of the Laplacian matrix of the manifold and the recovery of the signals from the noisy measurements. To improve computational effi

SUBMITTER: Poddar S 

PROVIDER: S-EPMC7990121 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature