Image Quality Optimization in 60 kVp Head-Neck CTA: A Comparative Study of FBP, ClearView, and ClearInfinity Reconstruction Algorithms

Abstract

Objective

To compare the impact of different reconstruction algorithms on the image quality of 60 kVp head and neck CT angiography (CTA) using subjective and objective metrics, with a focus on vessel edge sharpness.

Methods

This prospective study enrolled 45 patients who underwent ultra-low-voltage (60 kVp) head and neck CTA. Image datasets were reconstructed with filtered back-projection (FBP), ClearView (CV) and ClearInfinity (CI) algorithms at low (30%), medium (50%), and high (70%) strengths. Image quality was assessed subjectively and objectively via the Kruskal‒Wallis test for multiple comparisons. Objective parameters, including edge rise slope (ERS) and edge rise distance (ERD), were analyzed via the Friedman test of multiple comparisons statistics.

Results

Subjective assessments favored the CI50 reconstruction algorithm, demonstrating superior or satisfactory results compared to the other algorithms, with significantly better vessel delineation, edge definition and diagnostic confidence (all P < 0.05). Objective analysis revealed that the CV50 and CV70 algorithms significantly reduced ERS and/or elevated ERD (both P < 0.05). However, the CI50 algorithm maintained comparable vessel edge sharpness (P > 0.05) across all evaluated head and neck vascular segments when compared with the FBP algorithm.

Conclusions

The CI50 reconstruction algorithm optimizes image quality in 60 kVp head and neck CTA. It provides vessel edge sharpness comparable to FBP while offering superior vessel delineation, edge definition, and diagnostic confidence compared to FBP and CV algorithm. These findings suggest that CI50 has the potential to improve diagnostic accuracy in low-dose vascular imaging.

Keywords

Computed tomography angiography / Reconstruction algorithm / Deep learning reconstruction / Low-dose CT / Image quality / Vessel sharpness / 60 kVp / Heal-neck imaging

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Funding

National Key Research and Development Program of China(2024YFC2419300)

National Natural Science Foundation of China(82471967)

Hubei Provincial Key Research and Development Program(2024BCB008)

Natural Science Foundation of Hubei Province(2025AFB733)

RIGHTS & PERMISSIONS

The Author(s), under exclusive licence to the Huazhong University of Science and Technology