Reducing the number of samples in spatiotemporal dMRI acquisition design.
Algorithms
Animals
Computer Simulation
Corpus Callosum
/ diagnostic imaging
Diffusion
Diffusion Magnetic Resonance Imaging
Fourier Analysis
Image Interpretation, Computer-Assisted
/ methods
Mice
Mice, Inbred C57BL
Models, Statistical
Probability
Reproducibility of Results
Signal-To-Noise Ratio
Stochastic Processes
White Matter
/ diagnostic imaging
acquisition design
diffusion MRI
stochastic optimization
Journal
Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245
Informations de publication
Date de publication:
05 2019
05 2019
Historique:
received:
12
01
2018
revised:
17
10
2018
accepted:
18
10
2018
pubmed:
20
11
2018
medline:
25
3
2020
entrez:
20
11
2018
Statut:
ppublish
Résumé
Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. The aim of this paper is to bridge the gap between growing demands on spatiotemporal resolution of diffusion signal and the real-world time limitations. The authors introduce an acquisition scheme that reduces the number of samples under adjustable quality loss. Finding a sampling scheme that maximizes signal quality and satisfies given time constraints is NP-hard. Therefore, a heuristic method based on genetic algorithm is proposed in order to find suboptimal solutions in acceptable time. The analyzed diffusion signal representation is defined in the qτ space, so that it captures both spacial and temporal phenomena. The experiments on synthetic data and in vivo diffusion images of the C57Bl6 wild-type mouse corpus callosum reveal superiority of the proposed approach over random sampling and even distribution in the qτ space. The use of genetic algorithm allows to find acquisition parameters that guarantee high signal reconstruction accuracy under given time constraints. In practice, the proposed approach helps to accelerate the acquisition for the use of qτ-dMRI signal representation.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
3218-3233Informations de copyright
© 2018 International Society for Magnetic Resonance in Medicine.