Application of Ultra-high-resolution Scanning in Abdominal Artery CTA Using Photon-counting CT
Year: 2025
Type: article
Abstract: Objective: We evaluated the application of ultra-high-resolution scanning in abdominal artery computed tomography angiography (CTA) using photon-counting CT. Materials and Methods: We retrospectively collected data from 53 patients who underwent abdominal vascular CTA using photon-counting CT between October 2023 and February 2024. The arterial phase images were reconstructed as follows: (a) layer thickness and increment of 0.2 mm, reconstruction kernel Bv48 (body vascular, Bv), quantum iterative reconstruction (QIR) level 4; (b) layer thickness and increment of 1 mm, reconstruction kernel Bv48, QIR level 3. Two sets of images were reconstructed to generate axial, coronal, and volume-rendered (VR) images for subjective evaluation. The signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) of the abdominal aorta, celiac trunk, and common hepatic, splenic, superior mesenteric, and bilateral renal arteries were calculated. Three radiologists reviewed the images and evaluated image noise, vascular sharpness, overall image quality, and clarity of the main abdominal arteries. Results: The SNRs and CNRs of the 0.2 mm reconstructed images were lower than those of the 1 mm reconstructed images. However, the sharpness and overall image quality of the 0.2 mm reconstructed images were better than those of the 1 mm reconstructed images in subjective evaluations, especially in the display of small vessels in the VR image. Conclusion: Ultra-high-resolution scanning with photon-counting CT in abdominal vascular CTA effectively improves the display of small abdominal vessels, thereby optimizing surgical planning and reducing surgical bleeding and complications.
Authors: Lingyun WANG, Bowen SHI, Jiqiang LI, Haipeng DONG, Xiaoyu LV, Zhaocheng PAN
Institutions: Shanghai Jiao Tong University, Ruijin Hospital
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6887519268
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