A three-dimensional Magnetic Resonance Spin Tomography in Time-domain protocol for high-resolution multiparametric quantitative magnetic resonance imaging.
MR-STAT
multiparametric MRI
parallel imaging
quantitative MRI
relaxometry
Journal
NMR in biomedicine
ISSN: 1099-1492
Titre abrégé: NMR Biomed
Pays: England
ID NLM: 8915233
Informations de publication
Date de publication:
19 Oct 2023
19 Oct 2023
Historique:
revised:
04
08
2023
received:
10
05
2023
accepted:
11
09
2023
medline:
20
10
2023
pubmed:
20
10
2023
entrez:
19
10
2023
Statut:
aheadofprint
Résumé
Magnetic Resonance Spin TomogrAphy in Time-domain (MR-STAT) is a multiparametric quantitative MR framework, which allows for simultaneously acquiring quantitative tissue parameters such as T1, T2, and proton density from one single short scan. A typical two-dimensional (2D) MR-STAT acquisition uses a gradient-spoiled, gradient-echo sequence with a slowly varying RF flip-angle train and Cartesian readouts, and the quantitative tissue maps are reconstructed by an iterative, model-based optimization algorithm. In this work, we design a three-dimensional (3D) MR-STAT framework based on previous 2D work, in order to achieve better image signal-to-noise ratio, higher though-plane resolution, and better tissue characterization. Specifically, we design a 7-min, high-resolution 3D MR-STAT sequence, and the corresponding two-step reconstruction algorithm for the large-scale dataset. To reduce the long acquisition time, Cartesian undersampling strategies such as SENSE are adopted in our transient-state quantitative framework. To reduce the computational burden, a data-splitting scheme is designed for decoupling the 3D reconstruction problem into independent 2D reconstructions. The proposed 3D framework is validated by numerical simulations, phantom experiments, and in vivo experiments. High-quality knee quantitative maps with 0.8 × 0.8 × 1.5 mm
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e5050Subventions
Organisme : China Scholarship Council
ID : 201807720088
Informations de copyright
© 2023 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.
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