Data-driven optimization of sampling patterns for MR brain T_1ρ mapping.

ABSTRACT:

Purpose

The goal of this study was to apply a fast data-driven optimization algorithm, called bias-accelerated subset selection, for MR brain T_1ρ mapping to generate optimized sampling patterns (SPs) for compressed sensing reconstruction of brain 3D-T_1ρ MRI.

Methods

Five healthy volunteers were recruited, and fully sampled Cartesian 3D-T_1ρ MRIs were obtained. Variable density (VD) and Poisson disc (PD) undersampling was used as the input to SP optimization process. The reconstruction used 3 compressed sensing methods: spatiotemporal finite differences, low-rank plus sparse with spatial finite differences, and low rank. The performance of images and T_1ρ maps using PD-SP and VD-SP and their optimized sampling patterns (PD-OSP and VD-

SUBMITTER: Menon RG 

PROVIDER: S-EPMC10022748 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature