Artificial intelligence–based full aortic CT angiography imaging with ultra-low-dose contrast medium: a preliminary study

• Published in: European radiology Vol. 33; no. 1; pp. 678 - 689
• Main Authors: Zhou, Zhen, Gao, Yifeng, Zhang, Weiwei, Bo, Kairui, Zhang, Nan, Wang, Hui, Wang, Rui, Du, Zhiqiang, Firmin, David, Yang, Guang, Zhang, Heye, Xu, Lei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023; Springer Nature B.V
• Subjects: Algorithms; Angiography; Aorta; Aorta - diagnostic imaging; Aortic Diseases - diagnostic imaging; Artificial Intelligence; Computed Tomography; Computed Tomography Angiography - methods; Contrast Media; Datasets; Diagnostic Radiology; Diagnostic systems; Humans; Image contrast; Image quality; Image reconstruction; Imaging; Internal Medicine; Interventional Radiology; Medical diagnosis; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Parameters; Radiation Dosage; Radiographic Image Interpretation, Computer-Assisted - methods; Radiology; Ultrasound; Aortic CT angiography; Image quality; Contrast medium; Augmented cycle-consistent adversarial framework; Diagnostic accuracy
• ISSN: 1432-1084; 0938-7994; 1432-1084
• DOI: 10.1007/s00330-022-08975-1

Objectives 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. Methods 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. Results 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). Conclusions 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. Key Points • 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.