Noise reduction performance of a deep learning-based reconstruction in brain computed tomography images acquired with organ-based tube current modulation.
Computed tomography
Deep learning-based reconstruction
Image quality assessment
Low contrast
Organ-based tube current modulation
Journal
Physical and engineering sciences in medicine
ISSN: 2662-4737
Titre abrégé: Phys Eng Sci Med
Pays: Switzerland
ID NLM: 101760671
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
received:
01
02
2023
accepted:
17
05
2023
medline:
7
9
2023
pubmed:
2
6
2023
entrez:
2
6
2023
Statut:
ppublish
Résumé
We aimed to evaluate the image quality of brain computed tomography (CT) images reconstructed using deep learning-based reconstruction (DLR) in organ-based tube current modulation (OB-TCM) acquisition. An anthropomorphic head phantom and a cylindrical low-contrast phantom were scanned at the standard dose level for adult brain CT in axial volume acquisition without OB-TCM. Moreover, image acquisition with OB-TCM was performed. The radiation dose on the eye lens was measured using a scintillation fibre-optic dosimeter placed on the anthropomorphic phantom's eye surface. The task transfer function (TTF), contrast-to-noise ratio (CNR), and low-contrast object specific CNR obtained from low-contrast phantom images reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR), and two types of DLR (DLR
Identifiants
pubmed: 37266875
doi: 10.1007/s13246-023-01282-z
pii: 10.1007/s13246-023-01282-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1153-1162Informations de copyright
© 2023. Australasian College of Physical Scientists and Engineers in Medicine.
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