Ghorbannia Arash, Randles Amanda
Annu Int Conf IEEE Eng Med Biol Soc. 2024 Jul;2024:1-4. doi: 10.1109/EMBC53108.2024.10781665.
Coronary artery disease (CAD) is the most common form of cardiovascular disease, characterized by gradual narrowing of the artery walls due to plaque buildup. Computational fluid dynamics (CFD) is a non-invasive approach often used to investigate how these anatomical changes perturb local hemodynamics and contribute to the pathological mechanism of progression. Therefore, the accuracy of coronary tree alignment and anatomical feature detection is key to understanding these hemodynamically mediated mechanisms. Despite advances, current methods face challenges, such as the need for manual selection of landmarks, often resulting in a semi-automated experience. This study aims to improve this by developing a fully automated system to detect 3D anatomical characteristics and align coronary tree geometries in large clinical datasets. Our proposed algorithm enables full automatic placement of the corresponding centerline points and alignment evaluation through similarity-based assessment of Jaccard index (intersection over union) in a cohort of 73 coronary geometries.
冠状动脉疾病（CAD）是心血管疾病最常见的形式，其特征是由于斑块堆积导致动脉壁逐渐变窄。计算流体动力学（CFD）是一种非侵入性方法，常用于研究这些解剖学变化如何扰乱局部血流动力学并促成疾病进展的病理机制。因此，冠状动脉树对齐和解剖特征检测的准确性是理解这些血流动力学介导机制的关键。尽管取得了进展，但当前方法仍面临挑战，例如需要手动选择地标，这往往导致半自动化的体验。本研究旨在通过开发一个全自动系统来检测大型临床数据集中的3D解剖特征并对齐冠状动脉树几何形状，从而改善这一状况。我们提出的算法能够在73个冠状动脉几何形状的队列中，通过基于Jaccard指数（交集并集）的相似性评估，全自动放置相应的中心线点并进行对齐评估。
