Impact of Deep Learning Denoising Algorithm on Diffusion Tensor Imaging of the Growth Plate on Different Spatial Resolutions.
denoising
diffusion tensor imaging
growth
pediatrics
spatial resolution
voxel size
Journal
Tomography (Ann Arbor, Mich.)
ISSN: 2379-139X
Titre abrégé: Tomography
Pays: Switzerland
ID NLM: 101671170
Informations de publication
Date de publication:
02 Apr 2024
02 Apr 2024
Historique:
received:
21
02
2024
revised:
25
03
2024
accepted:
29
03
2024
medline:
26
4
2024
pubmed:
26
4
2024
entrez:
26
4
2024
Statut:
epublish
Résumé
To assess the impact of a deep learning (DL) denoising reconstruction algorithm applied to identical patient scans acquired with two different voxel dimensions, representing distinct spatial resolutions, this IRB-approved prospective study was conducted at a tertiary pediatric center in compliance with the Health Insurance Portability and Accountability Act. A General Electric Signa Premier unit (GE Medical Systems, Milwaukee, WI) was employed to acquire two DTI (diffusion tensor imaging) sequences of the left knee on each child at 3T: an in-plane 2.0 × 2.0 mm2 with section thickness of 3.0 mm and a 2 mm
Identifiants
pubmed: 38668397
pii: tomography10040039
doi: 10.3390/tomography10040039
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
504-519Subventions
Organisme : NIH HHS
ID : R01 HD104720
Pays : United States