Comparison of image quality in carotid dual-energy computed tomography angiography at 55 keV virtual monoenergetic imaging using deep learning and adaptive iterative reconstruction algorithm

Objectives: This study aims to evaluate the image quality of 55 keV virtual monoenergetic imaging (VMI) in carotid dual-energy computed tomography (CT) angiography (DE-CTA) reconstructed using deep learning image reconstruction (DLIR) algorithms and traditional iterative reconstruction algorithms. Material and Methods: This prospective study included 48 patients who underwent DE-CTA examinations at our institution between December 2024 and January 2025. Image reconstructions were performed using 50% strength adaptive statistical iterative reconstruction-Veo (ASIR-V 50%), low and high strengths DLIR (DLIR-L and DLIR-H) algorithms. Objective image quality was evaluated by measuring background noise (standard deviation), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at key anatomical locations, including the aortic arch, common carotid artery, carotid bifurcation, and internal carotid artery. Two senior radiologists conducted subjective assessments of image quality, focusing on image noise, artifacts, and vessel continuity, and the clarity of vascular wall margin. Results: Compared with ASIR-V 50% and DLIR-L, DLIR-H significantly improved image quality by reducing background noise and increasing SNR and CNR ( P < 0.05). Subjectively, DLIR-H images demonstrated better suppression of noise and clearer vascular wall margin ( P < 0.05). Subgroup analysis revealed that these improvements were more pronounced in patients with a body mass index (BMI) ≥24 kg/m 2 . No significant differences were observed in CT attenuations among the three reconstruction methods ( P > 0.05). Conclusion: At 55 keV VMI in carotid DE-CTA, DLIR-H significantly enhanced image quality, particularly by reducing noise and preserving fine anatomical structures. Its efficacy was especially notable in patients with BMI ≥24 kg/m 2 .