Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI.

Détails

ID Serval
serval:BIB_0F44BBEBD72C
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Sparse wars: A survey and comparative study of spherical deconvolution algorithms for diffusion MRI.
Périodique
NeuroImage
Auteur(s)
Canales-Rodríguez E.J., Legarreta J.H., Pizzolato M., Rensonnet G., Girard G., Patino J.R., Barakovic M., Romascano D., Alemán-Gómez Y., Radua J., Pomarol-Clotet E., Salvador R., Thiran J.P., Daducci A.
ISSN
1095-9572 (Electronic)
ISSN-L
1053-8119
Statut éditorial
Publié
Date de publication
01/01/2019
Peer-reviewed
Oui
Volume
184
Pages
140-160
Langue
anglais
Notes
Publication types: Comparative Study ; Journal Article ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Résumé
Spherical deconvolution methods are widely used to estimate the brain's white-matter fiber orientations from diffusion MRI data. In this study, eight spherical deconvolution algorithms were implemented and evaluated. These included two model selection techniques based on the extended Bayesian information criterion (i.e., best subset selection and the least absolute shrinkage and selection operator), iteratively reweighted l <sub>2</sub> - and l <sub>1</sub> -norm approaches to approximate the l <sub>0</sub> -norm, sparse Bayesian learning, Cauchy deconvolution, and two accelerated Richardson-Lucy algorithms. Results from our exhaustive evaluation show that there is no single optimal method for all different fiber configurations, suggesting that further studies should be conducted to find the optimal way of combining solutions from different methods. We found l <sub>0</sub> -norm regularization algorithms to resolve more accurately fiber crossings with small inter-fiber angles. However, in voxels with very dominant fibers, algorithms promoting more sparsity are less accurate in detecting smaller fibers. In most cases, the best algorithm to reconstruct fiber crossings with two fibers did not perform optimally in voxels with one or three fibers. Therefore, simplified validation systems as employed in a number of previous studies, where only two fibers with similar volume fractions were tested, should be avoided as they provide incomplete information. Future studies proposing new reconstruction methods based on high angular resolution diffusion imaging data should validate their results by considering, at least, voxels with one, two, and three fibers, as well as voxels with dominant fibers and different diffusion anisotropies.
Mots-clé
Algorithms, Bayes Theorem, Brain/anatomy & histology, Diffusion Magnetic Resonance Imaging/methods, Diffusion Tensor Imaging/methods, Humans, Image Processing, Computer-Assisted/methods, Reproducibility of Results, Signal Processing, Computer-Assisted, Surveys and Questionnaires, White Matter/anatomy & histology, Diffusion MRI, LASSO, Non-negative least squares, Sparse regression, Spherical deconvolution
Pubmed
Web of science
Création de la notice
19/09/2018 13:22
Dernière modification de la notice
26/03/2019 7:12
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