用于光子计数探测器CT钙定量分析的心脏虚拟非增强图像
Cardiac Virtual Noncontrast Images for Calcium Quantification with Photon-counting Detector CT.
作者信息
Mergen Victor, Ghouse Sadaf, Sartoretti Thomas, Manka Robert, Euler André, Kasel Albert M, Alkadhi Hatem, Eberhard Matthias
机构信息
From the Institute of Diagnostic and Interventional Radiology (V.M., S.G., T.S., R.M., A.E., H.A., M.E.) and Department of Cardiology, University Heart Center (R.M., A.M.K.), University Hospital Zurich, University of Zurich, Raemistrasse 100, CH-8091, Zurich, Switzerland; and Institute of Radiology, Spitäler fmi AG, Unterseen, Switzerland (M.E.).
出版信息
Radiol Cardiothorac Imaging. 2023 Jun 22;5(3):e220307. doi: 10.1148/ryct.220307. eCollection 2023 Jun.
PURPOSE
To assess the accuracy of aortic valve calcium (AVC), mitral annular calcium (MAC), and coronary artery calcium (CAC) quantification and risk stratification using virtual noncontrast (VNC) images from late enhancement photon-counting detector CT as compared with true noncontrast images.
MATERIALS AND METHODS
This retrospective, institutional review board-approved study evaluated patients undergoing photon-counting detector CT between January and September 2022. VNC images were reconstructed from late enhancement cardiac scans at 60, 70, 80, and 90 keV using quantum iterative reconstruction (QIR) strengths of 2-4. AVC, MAC, and CAC were quantified on VNC images and compared with quantification of AVC, MAC, and CAC on true noncontrast images using Bland-Altman analyses, regression models, intraclass correlation coefficients (ICC), and Wilcoxon tests. Agreement between severe aortic stenosis likelihood categories and CAC risk categories determined from VNC and true noncontrast images was assessed by weighted κ analysis.
RESULTS
Ninety patients were included (mean age, 80 years ± 8 [SD]; 49 male patients). Scores were similar on true noncontrast images and VNC images at 80 keV for AVC and MAC, regardless of QIR strengths, and VNC images at 70 keV with QIR 4 for CAC (all > .05). The best results were achieved using VNC images at 80 keV with QIR 4 for AVC (mean difference, 3; ICC = 0.992; = 0.98) and MAC (mean difference, 6; ICC = 0.998; = 0.99), and VNC images at 70 keV with QIR 4 for CAC (mean difference, 28; ICC = 0.996; = 0.99). Agreement between calcification categories was excellent on VNC images at 80 keV for AVC (κ = 0.974) and on VNC images at 70 keV for CAC (κ = 0.967).
CONCLUSION
VNC images from cardiac photon-counting detector CT enables patient risk stratification and accurate quantification of AVC, MAC, and CAC. Coronary Arteries, Aortic Valve, Mitral Valve, Aortic Stenosis, Calcifications, Photon-counting Detector CT © RSNA, 2023.
目的
评估使用来自晚期增强光子计数探测器CT的虚拟平扫（VNC）图像进行主动脉瓣钙化（AVC）、二尖瓣环钙化（MAC）和冠状动脉钙化（CAC）定量及风险分层的准确性，并与真实平扫图像进行比较。
材料与方法
这项经机构审查委员会批准的回顾性研究评估了2022年1月至9月期间接受光子计数探测器CT检查的患者。使用量子迭代重建（QIR）强度为2 - 4，从60、70、80和90 keV的晚期增强心脏扫描中重建VNC图像。在VNC图像上对AVC、MAC和CAC进行定量，并使用Bland - Altman分析、回归模型、组内相关系数（ICC）和Wilcoxon检验，将其与真实平扫图像上AVC、MAC和CAC的定量结果进行比较。通过加权κ分析评估从VNC图像和真实平扫图像确定的严重主动脉瓣狭窄可能性类别与CAC风险类别之间的一致性。
结果
纳入90例患者（平均年龄，80岁±8[标准差]；49例男性患者）。对于AVC和MAC，无论QIR强度如何，在80 keV的真实平扫图像和VNC图像上的评分相似；对于CAC，在70 keV且QIR为4的VNC图像上也是如此（所有P>0.05）。对于AVC，使用80 keV且QIR为4的VNC图像取得了最佳结果（平均差异，3；ICC = 0.992；P = 0.98），对于MAC也是如此（平均差异，6；ICC = 0.998；P = 0.99），对于CAC，使用70 keV且QIR为4的VNC图像取得了最佳结果（平均差异，28；ICC = 0.996；P = 0.99）。对于AVC，在80 keV的VNC图像上钙化类别之间的一致性极佳（κ = 0.974），对于CAC，在70 keV的VNC图像上也是如此（κ = 0.967）。
结论
心脏光子计数探测器CT的VNC图像能够实现患者风险分层以及对AVC、MAC和CAC进行准确量化。冠状动脉、主动脉瓣、二尖瓣、主动脉瓣狭窄、钙化、光子计数探测器CT © RSNA，2023。
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