Reducing the number of samples in spatiotemporal dMRI acquisition design.


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
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.

Identifiants

pubmed: 30450755
doi: 10.1002/mrm.27601
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

3218-3233

Informations de copyright

© 2018 International Society for Magnetic Resonance in Medicine.

Auteurs

Patryk Filipiak (P)

Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée, France.

Rutger Fick (R)

Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée, France.

Alexandra Petiet (A)

CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute, Paris, France.

Mathieu Santin (M)

CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute, Paris, France.

Anne-Charlotte Philippe (AC)

CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute, Paris, France.

Stephane Lehericy (S)

CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute, Paris, France.

Philippe Ciuciu (P)

Inria, CEA, Université Paris-Saclay, France.

Rachid Deriche (R)

Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée, France.

Demian Wassermann (D)

Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée, France.
Inria, CEA, Université Paris-Saclay, France.

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Classifications MeSH