Dynamic MR image reconstruction based on total generalized variation and low-rank decomposition.

ABSTRACT:

Purpose

Propose a novel decomposition-based model employing the total generalized variation (TGV) and the nuclear norm, which can be used in compressed sensing-based dynamic MR reconstructions.

Theory and methods

We employ the nuclear norm to represent the time-coherent background and the spatiotemporal TGV functional for the sparse dynamic component above. We first design an algorithm using the classical first-order primal-dual method for solving the proposed model and then give the norm estimation for the convergence condition. The proposed model is compared with the state-of-the-art methods on different data sets under different sampling schemes and acceleration factors.

Results

The proposed model achieves higher SERs and SSIMs than kt-SLR, kt-RPCA, L+S, and ICTGV

SUBMITTER: Wang D 

PROVIDER: S-EPMC7047634 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature