Application of Artificial Intelligence-based Image Optimization for Computed Tomography Angiography of the Aorta With Low Tube Voltage and Reduced Contrast Medium Volume

Purpose: The purpose of this study was to evaluate the impact of artificial intelligence (AI)-based noise reduction algorithm on aorta computed tomography angiography (CTA) image quality (IQ) at 80 kVp tube voltage and 40 mL contrast medium (CM). Materials and Methods: After obtaining institutional review board approval and 8 written informed consents, 60 patients (35 men, 25 women; age range: 18 to 85 y) referred for aorta CTA examination were assigned to 2 groups at random. Group A underwent an 80 kVp protocol with 40 mL CM (320 mg I/mL). Group A reconstructed with iterative reconstruction was named as group A1 and further AI-based noise reduction was named as group A2. Group B was scanned with standard 120 kVp, 80 mL CM, and iterative reconstruction algorithm. The quantitative assessment of IQ included aorta CT attenuation, noise, signal-to-noise ratio, and contrast-to-noise ratio. A 5-point scale (5-excellent, 1-poor) was used by 2 radiologists independently for qualitative IQ analysis. Results: The image noise significantly decreased while signal-to-noise ratio and contrast-to-noise ratio significantly increased in the order of group A1, B, and A2 (all P<0.05). Compared with group B, the subjective IQ score of group A1 was significantly lower (P<0.05), while that of group A2 had no significant difference (P>0.05). The effective dose and CM volume of group A were reduced by 79.18% and 50%, respectively, than that of group B. Conclusions: The AI-based noise reduction could improve the IQ of aorta CTA with low kV and reduced CM, which achieved the potential of radiation dose and contrast media reduction compared with conventional aorta CTA protocol.