ArticleNMR in biomedicine2020
High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.
Article in NMR in biomedicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Fast whole brain relaxometry with Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) at 3 T: a retrospective cohort study.Magma (New York, N.Y.) · 2025Article
- Efficient performance analysis and optimization of transient-state sequences for multiparametric magnetic resonance imaging.NMR in biomedicine · 2023Article
- Reducing the Complexity of Model-Based MRI Reconstructions via Sparsification.IEEE transactions on medical imaging · 2021Article
- Fast and accurate modeling of transient-state, gradient-spoiled sequences by recurrent neural networks.NMR in biomedicine · 2021Article
- High-resolution in vivo MR-STAT using a matrix-free and parallelized reconstruction algorithm.NMR in biomedicine · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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