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

arXiv: https://arxiv.org/abs/1904.13244 · HTML: https://mlsanity.com/paper/1904.13244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2019.

## Built on by (most cited first, 1 of 1)

- [Generalizable synthetic MRI with physics-informed convolutional networks](https://mlsanity.com/paper/2305.12570/) (2023)

## Abstract (arXiv)

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 × 1mm in-plane resolution) quantitative parameter maps in simulation, phantom and in-vivo brain experiments. Reconstructed T1 and T2 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.