Application value of high deep learning image reconstruction algorithm in “one-stop” dynamic CT myocardial perfusion
10.3760/cma.j.cn112149-20240222-00078
- VernacularTitle:高强度深度学习图像重建算法在“一站式”动态心肌灌注中的应用价值
- Author:
Xueyan MA
1
;
Yiran WANG
;
Jiawei LIU
;
Luotong WANG
;
Yonggao ZHANG
Author Information
1. 郑州大学第一附属医院放射科，郑州 450052
- Publication Type:Journal Article
- Keywords:
Tomography, X-ray computed;
Myocardial perfusion;
Image quality;
Deep learning image reconstruction;
Hybrid iterative reconstruction
- From:
Chinese Journal of Radiology
2025;59(1):36-42
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of high-level deep learning image reconstruction (DLIR-H) algorithm in dynamic myocardial perfusion (CTP) and coronary CT angiography (CCTA) extraction.Methods:From October 2021 to October 2022, 41 patients with confirmed or suspected coronary heart disease who underwent traditional CCTA and dynamic CTP examinations on GE Apex CT were prospectively collected. Traditional CCTA used 100 kVp tube voltage scan and DLIR-H to reconstruct the original image, while dynamic CTP used 80 kVp tube voltage scan to reconstruct the original image with ASiR-V 100% and DLIR-H, respectively. Comparing subjective and objective scores and myocardial blood flow (MBF) values?of rest and stress CTP between ASiR-V100% and DLIR-H. Subjective and objective scores, as well as stenosis degree and coronary CT blood flow reserve fraction (CT-FFR) value were analyzed on a vessel basis, and the image quality and diagnostic performance of traditional CCTA and single-phase CCTA (SP-CCTA) extracted under DLIR-H CTP were compared. Statistical analysis were performed using paired t test, Wilcoxon signed-rank test and χ2 test. Results:In the subjective image quality analysis of resting and stress CTP, DLIR-H was improved compared with ASiR-V100%, and the difference was statistically significant (all P<0.05). There was no significant difference in MBF values obtained by the two reconstruction methods in the assessment of quantitative myocardial perfusion ( P>0.05). Compared with traditional CCTA, the vascular CT value of SP-CCTA increased by 15.12%, the noise value increased by 32.27%, and the subjective score was also slightly lower (4.23±0.05). However, there were no statistically significant differences in total plaque volume, maximum stenosis degree, and number of CT-FFR positive vessels between SP-CCTA and traditional CCTA (all P>0.05). Conclusion:The deep learning reconstruction algorithm can not only improve the quality of the original image to a certain extent on dynamic CTP, but also extract high-quality single-phase CCTA to meet clinical diagnosis and realize a “one-stop” dynamic myocardial perfusion examination, which will help simplify the examination process, reduce contrast agent and radiation doses in the future.
