Impact of an advanced image based monoenergetic reconstruction algorithm on coronary artery stent visualization by dual source dual energy computed tomography: A primary study
Year: 2023
Type: article
Study design: Observational Study
Abstract: ABSTRACT Background: The aim of this study was to investigate the qualities of advanced image based virtual monoenergetic (Mono+) images using a noise optimized algorithm at different kiloelectron volts (keV) compared with linear blended 120 kilovoltage (kV) images in patients with coronary stents. Materials and Methods: Thirty two patients who had undergone coronary computed tomography angiography were retrospectively enrolled in the study. Linear blended 120 kV image and 16 Mono+ images were reconstructed. Quantitative assessments included luminal attenuation difference, in stent image noise, in stent signal to noise ratio (SNR) and contrast to noise ratio (CNR). Double blinded and independent qualitative evaluation of image quality was performed by two readers using a five point scale. Results: The luminal attenuation difference value in the 190 keV group was the lowest. The in stent image noise in the 190 keV group was the lowest, in comparison with that in the 90–180 keV groups; however, it showed no significant difference. The 40 keV group had the highest in stent SNR, and there were no differences between the 40 keV group and the 50–100 keV groups. The 60 keV group had the highest in stent CNR, although no significant difference was found between the 60 keV group and the 40–120 keV groups. The highest subjective score was observed in the 80 keV group, with significant differences from the other groups (P < 0.05), except for the 90 keV group. Conclusion: In comparison with computed tomography image with conventional reconstruction, Mono+ reconstructions at 90 keV can significantly reduce the in stent noise and provide better image quality of coronary stents.
Source: Digital Medicine
Authors: Lixiang Xie, Xiaoying Yang, Zhihan Xu, Zhongxiao Liu, Hao Wang, Ke Li, Mingming Zhao, Yankai Meng, Chunfeng Hu, Kai Xu
Institution: Affiliated Hospital of Xuzhou Medical College
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4385217038