Deep Generalization of Structured Low-Rank Algorithms (Deep-SLR).

ABSTRACT: Structured low-rank (SLR) algorithms, which exploit annihilation relations between the Fourier samples of a signal resulting from different properties, is a powerful image reconstruction framework in several applications. This scheme relies on low-rank matrix completion to estimate the annihilation relations from the measurements. The main challenge with this strategy is the high computational complexity of matrix completion. We introduce a deep learning (DL) approach to significantly reduce the computational complexity. Specifically, we use a convolutional neural network (CNN)-based filterbank that is trained to estimate the annihilation relations from imperfect (under-sampled and noisy) k-space measurements of Magnetic Resonance Imaging (MRI). The main reason for the computational effici

SUBMITTER: Pramanik A 

PROVIDER: S-EPMC7731895 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature