Kalykakis Georgios-Eleftherios, Antonopoulos Alexios S, Pitsargiotis Thomas, Siogkas Panagiotis, Exarchos Themistoklis, Kafouris Pavlos, Sakelarios Antonis, Liga Riccardo, Tzifa Aphrodite, Giannopoulos Andreas, Scholte Arthur J H A, Kaufmann Philipp A, Parodi Oberdan, Knuuti Juhani, Fotiadis Dimitrios I, Neglia Danilo, Anagnostopoulos Constantinos D
From the Department of Informatics, Ionian University, Kerkyra, Greece (G.E.K., T.E.); Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou St, 115 27 Athens, Greece (G.E.K., T.P., P.K., C.D.A.); CMR Unit, Royal Brompton Hospital, London, England (A.S.A.); Department of Mechanical Engineering and Aeronautics, University of Patras, Patras, Greece (T.P.); Department of Materials Science and Engineering University of Ioannina, Ioannina, Greece (P.S., D.I.F.); Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Athens, Greece (P.K.); Biomedical Research Institute-FORTH, Ioannina, Greece (A.S.); Cardiothoracic and Vascular Department, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy (R.L.); Division of Imaging Sciences and Biomedical Engineering, King's College London, London, England (A.T.); Cardiac Imaging (P.A.K.) Department of Nuclear Medicine (A.G.), University Hospital Zurich, Zurich, Switzerland (A.G.); Department of Cardiology, Heart Lung Center, Leiden University Medical Centre, Leiden, the Netherlands (A.J.H.A.S.); Institute of Clinical Physiology, National Research Council-CNR, Pisa, Italy (O.P., D.N.); Institute of Information Science and Technologies, National Research Council-CNR, Pisa, Italy (O.P.); PET Center, University Hospital and University of Turku, Turku, Finland (J.K.); Cardiovascular Department, Fondazione Toscana G. Monasterio, Pisa, Italy (D.N.); and Sant'Anna School of Advanced Studies, Pisa, Italy (D.N.).
Radiology. 2021 Sep;300(3):549-556. doi: 10.1148/radiol.2021204381. Epub 2021 Jun 29.
Background Advances in three-dimensional reconstruction techniques and computational fluid dynamics of coronary CT angiography (CCTA) data sets make feasible evaluation of endothelial shear stress (ESS) in the vessel wall. Purpose To investigate the relationship between CCTA-derived computational fluid dynamics metrics, anatomic and morphologic characteristics of coronary lesions, and their comparative performance in predicting impaired coronary vasodilating capability assessed by using PET myocardial perfusion imaging (MPI). Materials and Methods In this retrospective study, conducted between October 2019 and September 2020, coronary vessels in patients with stable chest pain and with intermediate probability of coronary artery disease who underwent both CCTA and PET MPI with oxygen 15-labeled water or nitrogen 13 ammonia and quantification of myocardial blood flow were analyzed. CCTA images were used in assessing stenosis severity, lesion-specific total plaque volume (PV), noncalcified PV, calcified PV, and plaque phenotype. PET MPI was used in assessing significant coronary stenosis. The predictive performance of the CCTA-derived parameters was evaluated by using area under the receiver operating characteristic curve (AUC) analysis. Results There were 92 coronary vessels evaluated in 53 patients (mean age, 65 years ± 7; 31 men). ESS was higher in lesions with greater than 50% stenosis versus those without significant stenosis (mean, 15.1 Pa ± 30 vs 4.6 Pa ± 4 vs 3.3 Pa ± 3; = .004). ESS was higher in functionally significant versus nonsignificant lesions (median, 7 Pa [interquartile range, 5-23 Pa] vs 2.6 Pa [interquartile range, 1.8-5 Pa], respectively; ≤ .001). Adding ESS to stenosis severity improved prediction (change in AUC, 0.10; 95% CI: 0.04, 0.17; = .002) for functionally significant lesions. Conclusion The combination of endothelial shear stress with coronary CT angiography (CCTA) stenosis severity improved prediction of an abnormal PET myocardial perfusion imaging result versus CCTA stenosis severity alone. © RSNA, 2021 See also the editorial by Kusmirek and Wieben in this issue.
背景 冠状动脉CT血管造影（CCTA）数据集的三维重建技术和计算流体动力学的进展使得评估血管壁内的内皮剪切应力（ESS）成为可能。目的 探讨CCTA衍生的计算流体动力学指标、冠状动脉病变的解剖学和形态学特征之间的关系，以及它们在预测使用PET心肌灌注成像（MPI）评估的冠状动脉扩张能力受损方面的比较性能。材料与方法 在这项回顾性研究中，于2019年10月至2020年9月期间，对患有稳定型胸痛且冠状动脉疾病可能性为中等的患者进行了CCTA和PET MPI检查，使用氧15标记水或氮13氨并对心肌血流进行定量分析，对其冠状动脉进行分析。CCTA图像用于评估狭窄严重程度、病变特异性总斑块体积（PV）、非钙化PV、钙化PV和斑块表型。PET MPI用于评估显著冠状动脉狭窄。通过使用受试者操作特征曲线（AUC）分析来评估CCTA衍生参数的预测性能。结果 对53名患者（平均年龄65岁±7岁；31名男性）的92条冠状动脉进行了评估。狭窄大于50%的病变的ESS高于无显著狭窄病变（平均值分别为15.1 Pa±30与4.6 Pa±4与3.3 Pa±3；P = 0.004）。功能上显著的病变的ESS高于非显著病变（中位数分别为7 Pa[四分位间距，5 - 23 Pa]与2.6 Pa[四分位间距，1.8 - 5 Pa]；P≤0.001）。对于功能上显著的病变，将ESS添加到狭窄严重程度可改善预测（AUC变化，0.10；95%CI：0.04，0.17；P = 0.002）。结论 与单独的CCTA狭窄严重程度相比，内皮剪切应力与冠状动脉CT血管造影（CCTA）狭窄严重程度的组合改善了对PET心肌灌注成像异常结果的预测。©RSNA，2021 另见本期Kusmirek和Wieben的社论。
