Deep Learning-based Noise Reduction for Fast Volume Diffusion Tensor Imaging: Assessing the Noise Reduction Effect and Reliability of Diffusion Metrics.
denoising approach with deep learning-based reconstruction
diffusion tensor imaging
diffusion tensor tractography
number of image acquisition
Journal
Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
ISSN: 1880-2206
Titre abrégé: Magn Reson Med Sci
Pays: Japan
ID NLM: 101153368
Informations de publication
Date de publication:
01 Dec 2021
01 Dec 2021
Historique:
pubmed:
24
9
2020
medline:
15
12
2021
entrez:
23
9
2020
Statut:
ppublish
Résumé
To assess the feasibility of a denoising approach with deep learning-based reconstruction (dDLR) for fast volume simultaneous multi-slice diffusion tensor imaging of the brain, noise reduction effects and the reliability of diffusion metrics were evaluated with 20 patients. Image noise was significantly decreased with dDLR. Although fractional anisotropy (FA) of deep gray matter was overestimated when the number of image acquisitions was one (NAQ1), FA in NAQ1 with dDLR became closer to that in NAQ5.
Identifiants
pubmed: 32963184
doi: 10.2463/mrms.tn.2020-0061
pmc: PMC8922344
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
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