Kyriakidis Savvas, Rigas George, Kigka Vassiliki, Zaridis Dimitris, Karanasiou Georgia, Tsompou Panagiota, Karanasiou Gianna, Lakkas Lampros, Nikopoulos Sotirios, Naka Katerina K, Michalis Lampros K, Fotiadis Dimitrios I, Sakellarios Antonis I
Department of Biomedical Research, Institute of Molecular Biology and Biotechnology-FORTH, University Campus of Ioannina, GR45110 Ioannina, Greece.
Unit of Medical Technology and Intelligent Information Systems, Department of Materials Science and Engineering, University of Ioannina, GR45110 Ioannina, Greece.
J Cardiovasc Dev Dis. 2023 Mar 19;10(3):130. doi: 10.3390/jcdd10030130.
Diagnosis of coronary artery disease is mainly based on invasive imaging modalities such as X-ray angiography, intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Computed tomography coronary angiography (CTCA) is also used as a non-invasive imaging alternative. In this work, we present a novel and unique tool for 3D coronary artery reconstruction and plaque characterization using the abovementioned imaging modalities or their combination. In particular, image processing and deep learning algorithms were employed and validated for the lumen and adventitia borders and plaque characterization at the IVUS and OCT frames. Strut detection is also achieved from the OCT images. Quantitative analysis of the X-ray angiography enables the 3D reconstruction of the lumen geometry and arterial centerline extraction. The fusion of the generated centerline with the results of the OCT or IVUS analysis enables hybrid coronary artery 3D reconstruction, including the plaques and the stent geometry. CTCA image processing using a 3D level set approach allows the reconstruction of the coronary arterial tree, the calcified and non-calcified plaques as well as the detection of the stent location. The modules of the tool were evaluated for efficiency with over 90% agreement of the 3D models with the manual annotations, while a usability assessment using external evaluators demonstrated high usability resulting in a mean System Usability Scale (SUS) score equal to 0.89, classifying the tool as "excellent".
冠状动脉疾病的诊断主要基于侵入性成像方式，如X射线血管造影、血管内超声（IVUS）和光学相干断层扫描（OCT）。计算机断层扫描冠状动脉造影（CTCA）也被用作一种非侵入性成像替代方法。在这项工作中，我们展示了一种新颖独特的工具，可使用上述成像方式或其组合进行三维冠状动脉重建和斑块特征分析。特别是，我们采用并验证了图像处理和深度学习算法，用于IVUS和OCT图像帧中的管腔和外膜边界以及斑块特征分析。还可从OCT图像中实现支架检测。X射线血管造影的定量分析能够实现管腔几何形状的三维重建和动脉中心线提取。将生成的中心线与OCT或IVUS分析结果相融合，可实现包括斑块和支架几何形状在内的混合冠状动脉三维重建。使用三维水平集方法进行CTCA图像处理，能够重建冠状动脉树、钙化和非钙化斑块以及检测支架位置。该工具的各个模块经评估效率较高，三维模型与手动标注的一致性超过90%，同时由外部评估人员进行的可用性评估显示其可用性较高，系统可用性量表（SUS）平均得分为0.89，将该工具归类为“优秀”。
