Automated inversion time selection for black-blood late gadolinium enhancement cardiac imaging in clinical practice.
Black-blood imaging
Gadolinium Enhancement
Magnetic resonance imaging
Myocardial infarction
Journal
Magma (New York, N.Y.)
ISSN: 1352-8661
Titre abrégé: MAGMA
Pays: Germany
ID NLM: 9310752
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
09
01
2023
accepted:
10
05
2023
revised:
25
04
2023
medline:
24
11
2023
pubmed:
9
6
2023
entrez:
9
6
2023
Statut:
ppublish
Résumé
To simplify black-blood late gadolinium enhancement (BL-LGE) cardiac imaging in clinical practice using an image-based algorithm for automated inversion time (TI) selection. The algorithm selects from BL-LGE TI scout images, the TI corresponding to the image with the highest number of sub-threshold pixels within a region of interest (ROI) encompassing the blood-pool and myocardium. The threshold value corresponds to the most recurrent pixel intensity of all scout images within the ROI. ROI dimensions were optimized in 40 patients' scans. The algorithm was validated retrospectively (80 patients) versus two experts and tested prospectively (5 patients) on a 1.5 T clinical scanner. Automated TI selection took ~ 40 ms per dataset (manual: ~ 17 s). Fleiss' kappa coefficient for automated-manual, intra-observer and inter-observer agreements were [Formula: see text]= 0.73, [Formula: see text] = 0.70 and [Formula: see text] = 0.63, respectively. The agreement between the algorithm and any expert was better than the agreement between the two experts or between two selections of one expert. Thanks to its good performance and simplicity of implementation, the proposed algorithm is a good candidate for automated BL-LGE imaging in clinical practice.
Identifiants
pubmed: 37294423
doi: 10.1007/s10334-023-01101-2
pii: 10.1007/s10334-023-01101-2
pmc: PMC10667449
doi:
Substances chimiques
Contrast Media
0
Gadolinium
AU0V1LM3JT
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
877-885Subventions
Organisme : Agence Nationale de la Recherche
ID : Equipex MUSIC ANR-11-EQPX-0030
Organisme : Agence Nationale de la Recherche
ID : ANR-21-CE17-0034-01
Organisme : Agence Nationale de la Recherche
ID : ANR-10-IAHU04-LIRYC
Organisme : Agence Nationale de la Recherche
ID : ANR-22-CPJ2-0009-01
Organisme : H2020 European Research Council
ID : n715093
Informations de copyright
© 2023. The Author(s).
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