Bayesian uncertainty quantification for magnetic resonance fingerprinting.

Algorithms Bayes Theorem Brain Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Phantoms, Imaging Uncertainty
Bayesian inference MRF uncertainty

Journal

Physics in medicine and biology
ISSN: 1361-6560
Titre abrégé: Phys Med Biol
Pays: England
ID NLM: 0401220

Informations de publication

Date de publication:
23 03 2021
Historique:
received: 23 09 2020
accepted: 01 03 2021
pubmed: 2 3 2021
medline: 25 12 2021
entrez: 1 3 2021
Statut: epublish

Résumé

Magnetic Resonance Fingerprinting (MRF) is a promising technique for fast quantitative imaging of human tissue. In general, MRF is based on a sequence of highly undersampled MR images which are analyzed with a pre-computed dictionary. MRF provides valuable diagnostic parameters such as the

Identifiants

DOI: 10.1088/1361-6560/abeae7 PMID: 33647894
pubmed: 33647894
doi: 10.1088/1361-6560/abeae7
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Creative Commons Attribution license.

Auteurs

Selma Metzner (S)

Physikalisch-Technische Bundesanstalt, Abbestraße 2-12, D-10587 Berlin, Germany.
ORCID: 0000-0001-8879-1895

Gerd Wübbeler (G)

Physikalisch-Technische Bundesanstalt, Abbestraße 2-12, D-10587 Berlin, Germany.

Sebastian Flassbeck (S)

Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, United States of America.
Center for Advanced Imaging Innovation and Research, New York University School of Medicine, New York, United States of America.

Constance Gatefait (C)

Physikalisch-Technische Bundesanstalt, Abbestraße 2-12, D-10587 Berlin, Germany.

Christoph Kolbitsch (C)

Physikalisch-Technische Bundesanstalt, Abbestraße 2-12, D-10587 Berlin, Germany.

Clemens Elster (C)

Physikalisch-Technische Bundesanstalt, Abbestraße 2-12, D-10587 Berlin, Germany.

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Classifications MeSH

questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Bayesian uncertainty quantification for...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Théorème de Bayes › Bayesian uncertainty quantification for...
questionsmedicales.fr › Système nerveux › Système nerveux central › Encéphale › Bayesian uncertainty quantification for...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Bayesian uncertainty quantification for...
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Traitement d'image par ordinateur › Bayesian uncertainty quantification for...
questionsmedicales.fr › Diagnostic › Techniques et procédures diagnostiques › Imagerie diagnostique › Tomographie › Imagerie par résonance magnétique › Bayesian uncertainty quantification for...
questionsmedicales.fr › Techniques d'investigation › Techniques de chimie analytique › Analyse spectrale › Spectroscopie par résonance magnétique › Bayesian uncertainty quantification for...
questionsmedicales.fr › Équipement et fournitures › Fantômes en imagerie › Bayesian uncertainty quantification for...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Incertitude › Bayesian uncertainty quantification for...