Comparison of Image Quality Reconstructed Using Iterative Reconstruction and Deep Learning Algorithms Under Varying...

Carotid CT angiography (CTA) is valuable for diagnosing carotid artery disease but involves radiation and contrast agent risks. Deep Learning Image Reconstruction (DLIR-H) shows potential for maintaining image quality in low-dose protocols. In this prospective study, 180 patients undergoing dual-energy CTA were divided into three groups: a control group (ASIR-V 50%, NI = 4, contrast = 0.5 mL/kg), a low-dose group (DLIR-H, NI = 11, contrast = 0.5 mL/kg), and an ultra-low-dose group (DLIR-H, NI = 13, contrast = 0.4 mL/kg). Objective (CTV[CT values], noise, SNR, CNR) and subjective (5-point Likert scale) image quality were evaluated. The ultra-low-dose group achieved a 20.3% reduction in contrast volume and a 53.3% reduction in effective dose compared to the control group (P < 0.001). Both experimental groups showed lower noise and higher CNR/SNR (except at aortic arch) than controls. However, the ultra-low-dose group had significantly lower CNR/SNR than the low-dose group (P < 0.05). Subjective image quality was superior in both experimental groups (P < 0.001), with high inter-rater agreement. DLIR-H outperformed ASIR-V in low and ultra-low-dose protocols but could not fully compensate for image quality degradation when radiation and contrast were further reduced.