Efficient total variation algorithm for fetal brain MRI reconstruction.

Détails

ID Serval
serval:BIB_C6ED63D2C46E
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Efficient total variation algorithm for fetal brain MRI reconstruction.
Périodique
Medical Image Computing and Computer-assisted Intervention : Miccai ... International Conference On Medical Image Computing and Computer-assisted Intervention
Auteur(s)
Tourbier S., Bresson X., Hagmann P., Thiran J.P., Meuli R., Cuadra M.B.
Statut éditorial
Publié
Date de publication
03/2014
Peer-reviewed
Oui
Volume
17
Numéro
Pt 2
Pages
252-259
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov't
Résumé
Fetal MRI reconstruction aims at finding a high-resolution image given a small set of low-resolution images. It is usually modeled as an inverse problem where the regularization term plays a central role in the reconstruction quality. Literature has considered several regularization terms s.a. Dirichlet/Laplacian energy, Total Variation (TV)- based energies and more recently non-local means. Although TV energies are quite attractive because of their ability in edge preservation, standard explicit steepest gradient techniques have been applied to optimize fetal-based TV energies. The main contribution of this work lies in the introduction of a well-posed TV algorithm from the point of view of convex optimization. Specifically, our proposed TV optimization algorithm or fetal reconstruction is optimal w.r.t. the asymptotic and iterative convergence speeds O(1/n2) and O(1/√ε), while existing techniques are in O(1/n2) and O(1/√ε). We apply our algorithm to (1) clinical newborn data, considered as ground truth, and (2) clinical fetal acquisitions. Our algorithm compares favorably with the literature in terms of speed and accuracy.
Mots-clé
Agenesis of Corpus Callosum/embryology, Agenesis of Corpus Callosum/pathology, Algorithms, Analysis of Variance, Brain/abnormalities, Brain/pathology, Data Interpretation, Statistical, Humans, Image Enhancement/methods, Image Interpretation, Computer-Assisted/methods, Magnetic Resonance Imaging/methods, Pattern Recognition, Automated/methods, Prenatal Diagnosis/methods, Reproducibility of Results, Sensitivity and Specificity
Pubmed
Création de la notice
15/12/2014 15:04
Dernière modification de la notice
03/03/2018 21:18
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