Optimizing visualization of thoracodorsal artery using energy spectrum CT angiography optimal single energy imaging combined with adaptive statistical iterative reconstruction V
10.13929/j.issn.1672-8475.2024.10.010
- VernacularTitle:能谱CT血管造影最佳单能量结合自适应统计迭代重建优化显示胸背动脉
- Author:
Jian HE
1
;
Yijun LIU
;
Wei WEI
;
Mengting HU
;
Yong FAN
;
Deshuo DONG
;
Changyu DU
Author Information
1. 大连医科大学附属第一医院放射科,辽宁 大连 116011
- Keywords:
tomography,X-ray computed;
thoracodorsal artery;
adaptive statistical iterative reconstruction;
prospective studies
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(10):613-617
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of energy spectrum CT angiography(CTA)optimal single energy imaging combined with adaptive statistical iterative reconstruction V(ASIR-V)for optimizing visualization of thoracodorsal artery(TDA).Methods Energy spectrum CTA was prospectively performed in 60 patients to observe TDA.The images were reconstructed as 120 kVp-like combined with 40%post-set ASIR-V(group A),as well as totally 18 kinds of single energy images ranging from 45 to 70 keV(with interval of 5 keV)combined with 40%,60%and 80%post-set ASIR-V(group B),and the subjective and objective evaluation results of the images were compared between and within groups.Results Under the same post-set ASIR-V weight,significant differences of subjective scores of axial and 3D images were found among different keV levels(all P＜0.001).With the increase of keV level,subjective scores of axial images increased first and then decreased,among which subjective score of 50 keV was the highest(all P＜0.001).Under the same keV levels,with the increase of ASIR-V weight,the subjective scores of overall axial images and 3D images for displaying the main trunk of TDA,as well as contrast-to-noise ratio of axillary artery increased(all P＜0.01).Conclusion Performing CTA using 50 keV single energy imaging combined with 80%ASIR-V reconstruction could balance image contrast and noise better,hence improving visualization of TDA and its branches.
