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 FFT. To do this at high-resolution, specialized algorithms are required to solve the underlying large-scale non-linear 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 ($1mm\times 1mm$ in-plane resolution) quantitative parameter maps in simulation, phantom and in-vivo brain experiments. Reconstructed $T_1$ and $T_2$ values for the gel phantoms are in agreement with results from gold standard measurements and for the in-vivo experiments the quantitative values show good agreement with literature values. In all experiments short pulse sequences with robust Cartesian sampling are used for which conventional MR Fingerprinting reconstructions are shown to fail.

arXiv commentAccepted by NMR in Biomedicine on 2019-12-05

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Cited by in corpus (4)

• Fast and Accurate Modeling of Transient-State Gradient-Spoiled Sequences by Recurrent Neural Networks
• Generalizable synthetic MRI with physics-informed convolutional networks
• Acceleration Strategies for MR-STAT: Achieving High-Resolution Reconstructions on a Desktop PC within 3 minutes
• A three-dimensional MR-STAT protocol for high-resolution multi-parametric quantitative MRI