Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods

• Published in: Frontiers in cardiovascular medicine Vol. 8; p. 597568
• Main Authors: Liu, Haipeng, Wingert, Aleksandra, Wang, Jian'an, Zhang, Jucheng, Wang, Xinhong, Sun, Jianzhong, Chen, Fei, Khalid, Syed Ghufran, Jiang, Jun, Zheng, Dingchang
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 10.02.2021
• Subjects: atherosclerosis; cardiac computed tomography; Cardiovascular Medicine; coronary artery disease; plaque morphology; three-dimensional reconstruction; atherosclerosis; coronary artery disease; plaque morphology; cardiac computed tomography; three-dimensional reconstruction
• ISSN: 2297-055X; 2297-055X
• DOI: 10.3389/fcvm.2021.597568

Atherosclerotic plaques are the major cause of coronary artery disease (CAD). Currently, computed tomography (CT) is the most commonly applied imaging technique in the diagnosis of CAD. However, the accurate extraction of coronary plaque geometry from CT images is still challenging. In this review, we focused on the methods in recent studies on the CT-based coronary plaque extraction. According to the dimension of plaque extraction method, the studies were categorized into two-dimensional (2D) and three-dimensional (3D) ones. In each category, the studies were analyzed in terms of data, methods, and evaluation. We summarized the merits and limitations of current methods, as well as the future directions for efficient and accurate extraction of coronary plaques using CT imaging. The methodological innovations are important for more accurate CT-based assessment of coronary plaques in clinical applications. The large-scale studies, de-blooming algorithms, more standardized datasets, and more detailed classification of non-calcified plaques could improve the accuracy of coronary plaque extraction from CT images. More multidimensional geometric parameters can be derived from the 3D geometry of coronary plaques. Additionally, machine learning and automatic 3D reconstruction could improve the efficiency of coronary plaque extraction in future studies.