Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm?
CT
Congenital heart disease
DLIR
Optimization
Pediatric
Phantom
Journal
Journal of cardiovascular computed tomography
ISSN: 1876-861X
Titre abrégé: J Cardiovasc Comput Tomogr
Pays: United States
ID NLM: 101308347
Informations de publication
Date de publication:
12 Mar 2024
12 Mar 2024
Historique:
received:
25
11
2023
revised:
27
02
2024
accepted:
01
03
2024
medline:
14
3
2024
pubmed:
14
3
2024
entrez:
13
3
2024
Statut:
aheadofprint
Résumé
ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose. To define the potential dose reduction using DLIR with an anthropomorphic phantom. An anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed. DLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50. DLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.
Sections du résumé
BACKGROUND
BACKGROUND
ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose.
OBJECTIVES
OBJECTIVE
To define the potential dose reduction using DLIR with an anthropomorphic phantom.
METHOD
METHODS
An anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed.
RESULTS
RESULTS
DLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50.
CONCLUSION
CONCLUSIONS
DLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.
Identifiants
pubmed: 38480035
pii: S1934-5925(24)00060-1
doi: 10.1016/j.jcct.2024.03.001
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest The authors have no conflicts of interest to disclose. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.