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基于深度学习重建的减影CT-FFR在钙化相关狭窄或支架植入患者中的诊断价值

Diagnostic value of deep learning reconstruction-based subtraction CT-FFR in patients with calcified-related stenosis or stent implantation.

作者信息

Xu Cheng, Yi Yan, Xu Min, Zou Li-Miao, Wang Ming, Wang Yun, Jin Zheng-Yu, Wang Yi-Ning

机构信息

Department of Radiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Canon Medical System, Beijing, China.

出版信息

Quant Imaging Med Surg. 2025 Feb 1;15(2):1599-1612. doi: 10.21037/qims-24-1366. Epub 2025 Jan 22.

Abstract

BACKGROUND

The application of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) is limited due to severe coronary calcium burden or stent implantation. This study aimed to explore the diagnostic value of subtraction CT-FFR with deep learning reconstruction (DLR) or hybrid iterative reconstruction (HIR) in detecting calcified-related hemodynamically significant stenosis, and the feasibility in the application of coronary stents.

METHODS

Between March 2020 and January 2022, consecutive patients with calcified-related stenosis or previous stent treatment who had undergone subtraction coronary computed tomography angiography (CTA) and invasive fractional flow reserve (FFR) were included in this prospective study. CT image data were reconstructed using HIR and DLR. The diagnostic performance of CT-FFR, and subtraction CT-FFR were evaluated. An FFR value of 0.8 or less was considered hemodynamically significant.

RESULTS

A total of 30 patients with 52 calcified-related lesions and 14 coronary stents were included in this study. Subtraction CT-FFR outperformed the corresponding CT-FFR in detecting calcified-related hemodynamically significant stenosis and in the application of coronary stents, while there was no significant difference when subtraction CT-FFR was compared with subtraction CT-FFR (P>0.05). Lesion-based analysis showed the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy for subtraction CT-FFR were 100.0%, 71.4%, 63.0%, 100% and 80.8%, respectively in detecting calcified-related hemodynamically significant stenosis, and were 100.0%, 83.3%, 88.9%, 100% and 92.9%, respectively in the application of coronary stents.

CONCLUSIONS

Subtraction CT-FFR yielded optimal diagnostic performance for hemodynamically significant calcified-related stenosis, and the application of subtraction CT-FFR in the evaluation of coronary stents was feasible. The diagnostic performance of subtraction CT-FFR was better than that of subtraction CT-FFR, but there was no significant difference.

摘要

背景

由于严重的冠状动脉钙化负荷或支架植入,冠状动脉计算机断层扫描血管造影衍生的血流储备分数(CT-FFR)的应用受到限制。本研究旨在探讨深度学习重建(DLR)或混合迭代重建(HIR)的减影CT-FFR在检测钙化相关的血流动力学显著狭窄中的诊断价值,以及在冠状动脉支架应用中的可行性。

方法

在2020年3月至2022年1月期间,本前瞻性研究纳入了连续的患有钙化相关狭窄或既往有支架治疗史且接受了冠状动脉计算机断层扫描血管造影(CTA)和有创血流储备分数(FFR)检查的患者。CT图像数据采用HIR和DLR进行重建。评估CT-FFR和减影CT-FFR的诊断性能。FFR值小于或等于0.8被认为具有血流动力学显著性。

结果

本研究共纳入30例患者,有52个钙化相关病变和14个冠状动脉支架。在检测钙化相关的血流动力学显著狭窄和冠状动脉支架应用方面,减影CT-FFR优于相应的CT-FFR,而减影CT-FFR与减影CT-FFR比较时无显著差异(P>0.05)。基于病变的分析显示,减影CT-FFR在检测钙化相关的血流动力学显著狭窄时的敏感性、特异性、阳性预测值(PPV)、阴性预测值(NPV)和准确性分别为100.0%、71.4%、63.0%、100%和80.8%,在冠状动脉支架应用中的相应值分别为100.0%、83.3%、88.9%、100%和92.9%。

结论

减影CT-FFR对血流动力学显著的钙化相关狭窄具有最佳诊断性能,减影CT-FFR在冠状动脉支架评估中的应用是可行的。减影CT-FFR的诊断性能优于减影CT-FFR,但无显著差异。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/00ad/11847182/5b9b258bf4ea/qims-15-02-1599-f1.jpg

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