Institute of Applied and Computational Mathematics, Foundation for Research and Technology-Hellas, Nikolaou Plastira 100, Vassilika Vouton, GR 700 13, Heraklion, Crete, Greece.
Cardiothoracic Surgery Unit, University General Hospital of Heraklion, Heraklion, Greece.
Med Biol Eng Comput. 2022 Jul;60(7):2095-2108. doi: 10.1007/s11517-022-02588-y. Epub 2022 May 17.
The instrumental role of comprehensive geometrical quantification in contemporary, effective descriptions of aortic growth and disease is well established. General or specific purpose algorithms are being developed to provide automatic landmark detection and high accuracy measurements. In the present study, an objective method for automated delineation of the ascending aorta is introduced, based on geometrical properties of the aortic wall. In the proximal ascending aorta, the method identifies the sinotubular junction by tracing the mean surface curvature transition region from the origins of the coronary arteries to the location where the aorta acquires its tubular shape. In the distal ascending aorta, the brachiocephalic artery origin is defined by a split centreline cross section within the brachiocephalic artery-aortic arch bifurcation region. The method's accuracy of detection was quantified against the manual border identification performed by two experienced observers on 3D aortic reconstructions of 44 computed tomography examinations. Median (method, observer) distance and inclination measurements ranged from 0.89 [1.02] mm and 4.66 [5.07]°, respectively, in the proximal border, to 2.18 [2.39] mm and 7.13 [4.77]° in the distal. Accuracy of border detection was found to be high compared to interobserver variability and relevant automatic and manual methodology results previously reported in literature. Delineation of the ascending aorta on a three-dimensional aortic reconstruction with automated identification of the sinotubular junction (proximal border) and of the origin of the brachiocephalic artery (distal border).
综合几何量化在当代有效描述主动脉生长和疾病中的工具作用已得到充分证实。目前正在开发通用或特定用途的算法，以提供自动地标检测和高精度测量。在本研究中，介绍了一种基于主动脉壁几何特性的自动勾画升主动脉的客观方法。在升主动脉近端，该方法通过追踪冠状动脉起源处到主动脉获得管状形状的位置之间的平均表面曲率过渡区域，来识别窦管交界处。在升主动脉远端，头臂干动脉起源于头臂干动脉-主动脉弓分叉区域的中心线横截面分割。该方法的检测准确性通过两名经验丰富的观察者在 44 次 CT 检查的 3D 主动脉重建上手动边界识别进行量化。近端边界的距离和倾斜测量中位数（方法，观察者）范围分别为 0.89 [1.02] mm 和 4.66 [5.07]°，远端边界的距离和倾斜测量中位数（方法，观察者）范围分别为 2.18 [2.39] mm 和 7.13 [4.77]°。与文献中先前报道的观察者间变异性以及相关的自动和手动方法结果相比，边界检测的准确性被认为很高。在三维主动脉重建上自动识别窦管交界处（近端边界）和头臂干动脉起源（远端边界）来勾画升主动脉。
