Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI.


Journal

Medical image analysis
ISSN: 1361-8423
Titre abrégé: Med Image Anal
Pays: Netherlands
ID NLM: 9713490

Informations de publication

Date de publication:
05 2023
Historique:
received: 26 07 2022
revised: 13 12 2022
accepted: 08 02 2023
medline: 21 4 2023
pubmed: 4 3 2023
entrez: 3 3 2023
Statut: ppublish

Résumé

We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from only axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown distribution of axonal orientations. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated although needed for modeling axons - especially in the context of multi-compartmental modeling. We introduce a new general method for the estimation of both the axial and radial axonal diffusivities at strong diffusion weightings based on kernel zonal modeling. The method could lead to estimates that are free from partial volume bias with gray matter or other isotropic compartments. The method is tested on publicly available data from the MGH Adult Diffusion Human Connectome project. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii from only two shells. The estimation problem is also addressed from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.

Identifiants

pubmed: 36867913
pii: S1361-8415(23)00028-2
doi: 10.1016/j.media.2023.102767
pii:
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

102767

Subventions

Organisme : NIMH NIH HHS
ID : U54 MH091657
Pays : United States

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Marco Pizzolato reports financial support was provided by EUFramework Programme for Research and Innovation Marie Sklodowska-Curie Actions. Erick Jorge Canales-Rodríguez reports financial support was provided by Swiss National Science Foundation. Mariam Andersson reports financial support was provided by Capital Region of Denmark. Tim B. Dyrby reports financial support was provided by the European Research Council.

Auteurs

Marco Pizzolato (M)

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark. Electronic address: mapiz@dtu.dk.

Erick Jorge Canales-Rodríguez (EJ)

Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.

Mariam Andersson (M)

Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.

Tim B Dyrby (TB)

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.

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Classifications MeSH