Artificial intelligence-based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study.
Aortic CT angiography
Augmented cycle-consistent adversarial framework
Contrast medium
Diagnostic accuracy
Image quality
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Jan 2023
Jan 2023
Historique:
received:
09
02
2022
accepted:
20
06
2022
revised:
16
05
2022
pubmed:
6
7
2022
medline:
20
12
2022
entrez:
5
7
2022
Statut:
ppublish
Résumé
To further reduce the contrast medium (CM) dose of full aortic CT angiography (ACTA) imaging using the augmented cycle-consistent adversarial framework (Au-CycleGAN) algorithm. We prospectively enrolled 150 consecutive patients with suspected aortic disease. All received ACTA scans of ultra-low-dose CM (ULDCM) protocol and low-dose CM (LDCM) protocol. These data were randomly assigned to the training datasets (n = 100) and the validation datasets (n = 50). The ULDCM images were reconstructed by the Au-CycleGAN algorithm. Then, the AI-based ULDCM images were compared with LDCM images in terms of image quality and diagnostic accuracy. The mean image quality score of each location in the AI-based ULDCM group was higher than that in the ULDCM group but a little lower than that in the LDCM group (all p < 0.05). All AI-based ULDCM images met the diagnostic requirements (score ≥ 3). Except for the image noise, the AI-based ULDCM images had higher attenuation value than the ULDCM and LDCM images as well as higher SNR and CNR in all locations of the aorta analyzed (all p < 0.05). Similar results were also seen in obese patients (BMI > 25, all p < 0.05). Using the findings of LDCM images as the reference, the AI-based ULDCM images showed good diagnostic parameters and no significant differences in any of the analyzed aortic disease diagnoses (all K-values > 0.80, p < 0.05). The required dose of CM for full ACTA imaging can be reduced to one-third of the CM dose of the LDCM protocol while maintaining image quality and diagnostic accuracy using the Au-CycleGAN algorithm. • The required dose of contrast medium (CM) for full ACTA imaging can be reduced to one-third of the CM dose of the low-dose contrast medium (LDCM) protocol using the Au-CycleGAN algorithm. • Except for the image noise, the AI-based ultra-low-dose contrast medium (ULDCM) images had better quantitative image quality parameters than the ULDCM and LDCM images. • No significant diagnostic differences were noted between the AI-based ULDCM and LDCM images regarding all the analyzed aortic disease diagnoses.
Identifiants
pubmed: 35788754
doi: 10.1007/s00330-022-08975-1
pii: 10.1007/s00330-022-08975-1
doi:
Substances chimiques
Contrast Media
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
678-689Subventions
Organisme : the Capital's Funds for Health Improvement and Research Foundation of China
ID : 2020-1-1052
Organisme : British Heart Foundation
ID : PG/16/78/32402
Pays : United Kingdom
Organisme : the National Key Research and Development Program of China
ID : 2016YFC1300300
Organisme : the National Natural Science Foundation of China
ID : U1908211
Organisme : Medical Research Council
ID : MR/V023799/1
Pays : United Kingdom
Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2022. The Author(s), under exclusive licence to European Society of Radiology.
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