Application of New Iterative Reconstruction Algorithm Based on Artificial Intelligence and Low Tube Voltage in Reducing the Radiation Dose of Coronary CTA in Obese Patients

Objective: To explore the feasibility of using a new artificial intelligence (AI)-based iterative reconstruction algorithm with low tube voltage to reduce the radiation dose of coronary computed tomography angiography (CTA) in obese patients. Methods: A total of 40 patients were randomly divided into routine-dose (group A n=20) and low-dose groups (group B n=20). In group A, a tube voltage of 120 kV and a tube current of 200 mAs, whereas in group B, a tube voltage of 80 kV and a tube current of 200 mAs were used. The collected data was transferred to the post-processing workstation. The image reconstructions of the conventional and new iterative reconstruction algorithms based on AI were used. Comparative analysis of average CT value of the aorta, signal to noise ratio (SNR) and contrast to noise ratio (CNR) of the aorta and left coronary, SNR and CNR of right coronary in the two groups. Results: The average CT value of the aorta in group B, the SNR and CNR of the aorta and the left coronary artery, the SNR and CNR of the right coronary artery was not significantly different from those of group A. There was no difference in the subjective image quality between the two groups. However, the radiation dose in patients of group B was reduced by 68.2% compared with that of group A. Conclusion: The new iterative reconstruction algorithm can significantly improve the image quality of the reconstruction. The radiation dose can be significantly reduced with a tube voltage of 80 kVp, and the coronary CTA image equivalent to the image quality of the conventional dose can be obtained.