High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.

ABSTRACT: MR-STAT is a recently proposed framework that allows the reconstruction of multiple quantitative parameter maps from a single short scan by performing spatial localisation and parameter estimation on the time-domain data simultaneously, without relying on the fast Fourier transform (FFT). To do this at high resolution, specialized algorithms are required to solve the underlying large-scale nonlinear optimisation problem. We propose a matrix-free and parallelized inexact Gauss-Newton based reconstruction algorithm for this purpose. The proposed algorithm is implemented on a high-performance computing cluster and is demonstrated to be able to generate high-resolution (1 mm  ×  1 mm in-plane resolution) quantitative parameter maps in simulation, phantom, and in vivo brain experiments. Reconst

SUBMITTER: van der Heide O 

PROVIDER: S-EPMC7079175 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature