A CT deep learning reconstruction algorithm: Image quality evaluation for brain protocol at decreasing dose indexes in comparison with FBP and statistical iterative reconstruction algorithms.
Brain protocol
Image quality
Philips Precise Image
deep learning CT image reconstruction
Journal
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888
Informations de publication
Date de publication:
28 Feb 2024
28 Feb 2024
Historique:
received:
25
06
2023
revised:
17
01
2024
accepted:
09
02
2024
medline:
1
3
2024
pubmed:
1
3
2024
entrez:
29
2
2024
Statut:
aheadofprint
Résumé
To characterise the impact of Precise Image (PI) deep learning reconstruction algorithm on image quality, compared to filtered back-projection (FBP) and iDose Catphan-600 phantom was acquired with an Incisive CT scanner using a dedicated brain protocol, at six different dose levels (volume computed tomography dose index (CTDI The five PI levels did not significantly affect the mean CT number. For a given CTDI The improved performances of intermediate PI levels in brain protocols compared to conventional algorithms seem to suggest a potential reduction of CTDI
Identifiants
pubmed: 38422902
pii: S1120-1797(24)00113-3
doi: 10.1016/j.ejmp.2024.103319
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
103319Informations de copyright
Copyright © 2024. Published by Elsevier Ltd.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.