Physical and digital phantoms for validating tractography and assessing artifacts.


Journal

NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515

Informations de publication

Date de publication:
15 12 2021
Historique:
received: 12 04 2021
revised: 01 10 2021
accepted: 01 11 2021
pubmed: 9 11 2021
medline: 11 2 2022
entrez: 8 11 2021
Statut: ppublish

Résumé

Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.

Identifiants

pubmed: 34748954
pii: S1053-8119(21)00976-9
doi: 10.1016/j.neuroimage.2021.118704
pii:
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

118704

Informations de copyright

Copyright © 2021. Published by Elsevier Inc.

Auteurs

Ivana Drobnjak (I)

Center for Medical Image Computing, Department of Computer Science, University College London, UK. Electronic address: i.drobnjak@ucl.ac.uk.

Peter Neher (P)

Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Cyril Poupon (C)

BAOBAB, NeuroSpin, Commissariat à l'Energie Atomique, Institut des Sciences du Vivant Frédéric Joliot, Gif-sur-Yvette, France.

Tabinda Sarwar (T)

School of Computing Technologies, RMIT University, Australia.

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