TY - JOUR
T1 - Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging
T2 - A Review of Recent Methods
AU - Liu, Haipeng
AU - Wingert, Aleksandra
AU - Wang, Jian'an
AU - Zhang, Jucheng
AU - Wang, Xinhong
AU - Sun, Jianzhong
AU - Chen, Fei
AU - Khalid, Syed Ghufran
AU - Jiang, Jun
AU - Zheng, Dingchang
N1 - Copyright © 2021 Liu, Wingert, Wang, Zhang, Wang, Sun, Chen, Khalid, Jiang and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
PY - 2021/2/10
Y1 - 2021/2/10
N2 - Background: 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. Summary of Review: 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. Conclusion: 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.
AB - Background: 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. Summary of Review: 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. Conclusion: 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.
KW - coronary artery disease
KW - atherosclerosis
KW - plaque morphology
KW - cardiac computed tomography
KW - three-dimensional reconstruction
UR - http://www.scopus.com/inward/record.url?scp=85115416449&partnerID=8YFLogxK
U2 - 10.3389/fcvm.2021.597568
DO - 10.3389/fcvm.2021.597568
M3 - Review article
SN - 2297-055X
VL - 8
JO - Frontiers in Cardiovascular Medicine
JF - Frontiers in Cardiovascular Medicine
M1 - 597568
ER -