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

## TL;DR

This paper introduces a novel matrix-free, parallelized reconstruction algorithm for high-resolution MR-STAT imaging, enabling accurate quantitative parameter maps from short scans without relying on FFT, demonstrated through simulations, phantoms, and in-vivo brain experiments.

## Contribution

It presents a new inexact Gauss-Newton based algorithm that efficiently reconstructs high-resolution quantitative maps in MR-STAT without matrix operations, suitable for parallel computing.

## Key findings

- Achieved 1mm resolution in simulations, phantoms, and in-vivo brain scans.
- Reconstructed T1 and T2 values match standard measurements and literature.
- Conventional MR Fingerprinting fails with the used pulse sequences.

## 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.