Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord.


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:
02 2019
Historique:
received: 09 03 2018
revised: 28 06 2018
accepted: 29 06 2018
pubmed: 20 9 2018
medline: 18 12 2019
entrez: 20 9 2018
Statut: ppublish

Résumé

Time-dependence is a key feature of the diffusion-weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time-dependence in the human spinal cord, as its axonal structure is specific and different from the brain. We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi-shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm Both intra-/extra-axonal perpendicular diffusivities and kurtosis excess show time-dependence in our synthetic spinal cord model. This time-dependence is reflected mostly in the intra-axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time-dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time-dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two-compartment SMT metrics. Accounting for large axon calibers improves fitting of multi-compartment models to a minor extent. Time-dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non-invasive estimation of microstructural properties. The time-dependence of the perpendicular DW signal may feature strong intra-axonal contributions due to large spinal axon caliber. Hence, a popular model known as "stick" (zero-radius cylinder) may be sub-optimal to describe signals from the largest spinal axons.

Identifiants

pubmed: 30229564
doi: 10.1002/mrm.27463
pmc: PMC6586052
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1247-1264

Subventions

Organisme : Horizon 2020 (H2020) Framework Programme CDS-QuaMRI
ID : 634541
Pays : International
Organisme : Engineering and Physical Sciences Research Council (EPSRC)
Pays : International
Organisme : Platform Grant for medical image computing for next-generation healthcare technology
ID : EP/M020533/1
Pays : International
Organisme : European Committee for Research and Treatment in Multiple Sclerosis (ECTRIMS)
Pays : International
Organisme : EPSRC
ID : G007748
Pays : International
Organisme : EPSRC
ID : M507970
Pays : International
Organisme : EPSRC
ID : I027084
Pays : International
Organisme : EPSRC
ID : M020533
Pays : International
Organisme : EPSRC
ID : N018702
Pays : International
Organisme : EPSRC
ID : H2020-EU.3.1
Pays : International
Organisme : EPSRC
ID : 666992-2
Pays : International
Organisme : EPSRC
ID : 634541
Pays : International
Organisme : EPSRC
ID : EP/I027084/1
Pays : International
Organisme : International Spinal Research Trust (UK)
Pays : International
Organisme : Wings for Life (Austria)
Pays : International
Organisme : Craig H. Neilsen Foundation (USA)
ID : H2020-EU.3.1 (634541)
Pays : International
Organisme : UK Multiple Sclerosis Society
ID : 892/08
Pays : International
Organisme : Department of Health's National Institute for Health Research Biomedical Research Centres
ID : BRC R&D 03/10/RAG0449
Pays : International

Informations de copyright

© 2018 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.

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Auteurs

Francesco Grussu (F)

Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.
Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Andrada Ianuş (A)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Champalimaud Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal.

Carmen Tur (C)

Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.

Ferran Prados (F)

Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.
Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.

Torben Schneider (T)

Philips United Kingdom, Guildford, Surrey, United Kingdom.

Enrico Kaden (E)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Sébastien Ourselin (S)

Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom.

Ivana Drobnjak (I)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Hui Zhang (H)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.

Daniel C Alexander (DC)

Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
Clinical Imaging Research Centre, National University of Singapore, Singapore, Singapore.

Claudia A M Gandini Wheeler-Kingshott (CAM)

Queen Square MS Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.
Brain MRI 3T Research Centre, C. Mondino National Neurological Institute, Pavia, Italy.
Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.

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