4.6 Review

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

Journal

FRONTIERS IN CARDIOVASCULAR MEDICINE
Volume 8, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fcvm.2021.597568

Keywords

coronary artery disease; atherosclerosis; plaque morphology; cardiac computed tomography; three-dimensional reconstruction

Funding

  1. Newton Funds Industry Academia Partnership Programme [IAPP1R2\100204]
  2. Zhejiang Provincial Department of Science and Technology [2020C03016]
  3. High-level University Fund of Southern University of Science of Technology [G02236002]
  4. National Key Research and Development Program of China [2018YFE0198400]

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The review focuses on recent studies of CT-based coronary plaque extraction, categorizing them into 2D and 3D methods. The analysis of data, methods, and evaluation in each category highlights the need for methodological innovations to improve accuracy in clinical applications. Advanced techniques such as de-blooming algorithms, standardized datasets, and machine learning could enhance the efficiency and accuracy of coronary plaque extraction in future studies.
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.

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