Comparison of Image Quality Reconstructed Using Iterative Reconstruction and Deep Learning Algorithms Under Varying Dose Reductions in Dual-Energy Carotid CT Angiography.
Authors
Affiliations (7)
Affiliations (7)
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China.
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China.
- Jiangsu Provincial Engineering Research Center for Medical Imaging and Digital Medicine, Xuzhou, Jiangsu, 221002, China.
- CT Imaging Research Center, GE HealthCare China, Shanghai, China.
- Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China. [email protected].
- School of Medical Imaging, Xuzhou Medical University, Xuzhou, Jiangsu, 221002, China. [email protected].
- Jiangsu Provincial Engineering Research Center for Medical Imaging and Digital Medicine, Xuzhou, Jiangsu, 221002, China. [email protected].
Abstract
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.