Dual-Low Technology in Coronary and Abdominal CT Angiography: A Comparative Study of Deep Learning Image Reconstruction and Adaptive Statistic Iterative Reconstruction-Veo

Abstract

To investigate the application advantages of dual-low technology (low radiation dose and low contrast agent dose) in deep learning image reconstruction (DLIR) compared to the adaptive statistical iterative reconstruction-Veo (ASIR-V) standard protocol when combing coronary computed tomography angiography (CCTA) and abdominal computed tomography angiography (ACTA). Sixty patients who underwent CCTA and ACTA were recruited. Thirty patients with low body mass index (BMI) (< 24 kg/m², Group A, standard protocol) were reconstructed using 60% ASIR-V, and 30 patients with high BMI (> 24 kg/m², Group B, dual-low protocol) were reconstructed using DLIR at high strength (DLIR-H). The effective dose and contrast agent dose were recorded. The CT values, standard deviations, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. The subjective evaluation criteria were scored by two radiologists using a blind Likert 5-point scale. The general data, objective evaluation, and subjective scores between both groups were compared using corresponding test methods. The consistency of objective and subjective evaluations between the two radiologists were analyzed using Kappa tests. Group B showed a remarkable 44.6% reduction in mean effective dose (p < 0.01) and a 20.3% decrease in contrast agent dose compared to Group A (p < 0.01). The DLIR-H demonstrated the smallest standard deviations and highest SNR and CNR values (p < 0.01). The subjective score of DLIR-H was the highest (p < 0.01), and there was good consistency between the two radiologists in the subjective scoring of CCTA and ACTA image quality (κ = 0.751 ~ 0.919, p < 0.01). In combined CCTA and ACTA protocols, DLIR can significantly reduce the effective dose and contrast agent dose compared to ASIR-V while maintaining good image quality.