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冠状动脉计算机断层血管造影钙化积分:一种新型虚拟非碘重建算法在临床第一代双源光子计数探测器系统上的体外和体内验证。

Coronary Computed Tomography Angiography-Based Calcium Scoring: In Vitro and In Vivo Validation of a Novel Virtual Noniodine Reconstruction Algorithm on a Clinical, First-Generation Dual-Source Photon Counting-Detector System.

机构信息

From the Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston.

Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany.

出版信息

Invest Radiol. 2022 Aug 1;57(8):536-543. doi: 10.1097/RLI.0000000000000868. Epub 2022 Mar 21.

Abstract

PURPOSE

The aim of this study was to evaluate coronary computed tomography angiography (CCTA)-based in vitro and in vivo coronary artery calcium scoring (CACS) using a novel virtual noniodine reconstruction (PureCalcium) on a clinical first-generation photon-counting detector-computed tomography system compared with virtual noncontrast (VNC) reconstructions and true noncontrast (TNC) acquisitions.

MATERIALS AND METHODS

Although CACS and CCTA are well-established techniques for the assessment of coronary artery disease, they are complementary acquisitions, translating into increased scan time and patient radiation dose. Hence, accurate CACS derived from a single CCTA acquisition would be highly desirable. In this study, CACS based on PureCalcium, VNC, and TNC, reconstructions was evaluated in a CACS phantom and in 67 patients (70 [59/80] years, 58.2% male) undergoing CCTA on a first-generation photon counting detector-computed tomography system. Coronary artery calcium scores were quantified for the 3 reconstructions and compared using Wilcoxon test. Agreement was evaluated by Pearson and Spearman correlation and Bland-Altman analysis. Classification of coronary artery calcium score categories (0, 1-10, 11-100, 101-400, and >400) was compared using Cohen κ .

RESULTS

Phantom studies demonstrated strong agreement between CACS PureCalcium and CACS TNC (60.7 ± 90.6 vs 67.3 ± 88.3, P = 0.01, r = 0.98, intraclass correlation [ICC] = 0.98; mean bias, 6.6; limits of agreement [LoA], -39.8/26.6), whereas CACS VNC showed a significant underestimation (42.4 ± 75.3 vs 67.3 ± 88.3, P < 0.001, r = 0.94, ICC = 0.89; mean bias, 24.9; LoA, -87.1/37.2). In vivo comparison confirmed a high correlation but revealed an underestimation of CACS PureCalcium (169.3 [0.7/969.4] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.98; mean bias, -113.5; LoA, -470.2/243.2). In comparison, CACS VNC showed a similarly high correlation, but a substantially larger underestimation (24.3 [0/272.3] vs 232.2 [26.5/1112.2], P < 0.001, r = 0.97, ICC = 0.54; mean bias, -551.6; LoA, -2037.5/934.4). CACS PureCalcium showed superior agreement of CACS classification ( κ = 0.88) than CACS VNC ( κ = 0.60).

CONCLUSIONS

The accuracy of CACS quantification and classification based on PureCalcium reconstructions of CCTA outperforms CACS derived from VNC reconstructions.

摘要

目的

本研究旨在评估基于新型虚拟非碘重建(PureCalcium)的冠状动脉计算机断层血管造影术(CCTA)在体外和体内冠状动脉钙化评分(CACS)的准确性,该方法是在临床第一代光子计数探测器 CT 系统上进行的,与虚拟非对比(VNC)重建和真实非对比(TNC)采集进行比较。

材料和方法

虽然 CACS 和 CCTA 是评估冠状动脉疾病的成熟技术,但它们是互补的采集,这会导致扫描时间和患者辐射剂量增加。因此,从单次 CCTA 采集获得准确的 CACS 将是非常理想的。在这项研究中,我们在 CACS 体模和 67 名(70 岁[59/80 岁],58.2%为男性)接受第一代光子计数探测器 CT 系统 CCTA 的患者中评估了基于 PureCalcium、VNC 和 TNC 重建的 CACS。使用 Wilcoxon 检验比较 3 种重建的冠状动脉钙评分,并进行了比较。使用 Pearson 和 Spearman 相关性和 Bland-Altman 分析评估一致性。使用 Cohen κ 比较冠状动脉钙评分类别(0、1-10、11-100、101-400 和>400)的分类。

结果

体模研究表明,CACS PureCalcium 与 CACS TNC 之间具有很强的一致性(60.7±90.6 vs 67.3±88.3,P=0.01,r=0.98,组内相关系数[ICC]=0.98;平均偏差 6.6;一致性限[LoA],-39.8/26.6),而 CACS VNC 则显示出明显的低估(42.4±75.3 vs 67.3±88.3,P<0.001,r=0.94,ICC=0.89;平均偏差 24.9;LoA,-87.1/37.2)。体内比较证实了高度相关性,但显示出 CACS PureCalcium 的低估(169.3[0.7/969.4] vs 232.2[26.5/1112.2],P<0.001,r=0.97,ICC=0.98;平均偏差-113.5;LoA,-470.2/243.2)。相比之下,CACS VNC 显示出相似的高相关性,但存在更大的低估(24.3[0/272.3] vs 232.2[26.5/1112.2],P<0.001,r=0.97,ICC=0.54;平均偏差-551.6;LoA,-2037.5/934.4)。CACS PureCalcium 的 CACS 分类一致性( κ=0.88)优于 CACS VNC( κ=0.60)。

结论

基于 CCTA 的 PureCalcium 重建的 CACS 定量和分类的准确性优于基于 VNC 重建的 CACS。

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