Future Directions in Coronary CT Angiography: CT-Fractional Flow Reserve, Plaque Vulnerability, and Quantitative Plaque Assessment
- Author:
Fernando Uliana KAY
1
;
Arzu CANAN
;
Suhny ABBARA
Author Information
- Publication Type:Review
- Keywords: Coronary computed tomography angiography; Coronary plaque; Fractional flow reserve; Plaque characterization; Plaque volume
- MeSH: Acute Coronary Syndrome; Angiography; Constriction, Pathologic; Coronary Artery Disease; Coronary Vessels; Heart; Hemodynamics; Humans; Hydrodynamics; Machine Learning; Myocardial Ischemia
- From:Korean Circulation Journal 2020;50(3):185-202
- CountryRepublic of Korea
- Language:English
- Abstract: Coronary computed tomography angiography (CCTA) is a well-validated and noninvasive imaging modality for the assessment of coronary artery disease (CAD) in patients with stable ischemic heart disease and acute coronary syndromes (ACSs). CCTA not only delineates the anatomy of the heart and coronary arteries in detail, but also allows for intra- and extraluminal imaging of coronary arteries. Emerging technologies have promoted new CCTA applications, resulting in a comprehensive assessment of coronary plaques and their clinical significance. The application of computational fluid dynamics to CCTA resulted in a robust tool for noninvasive assessment of coronary blood flow hemodynamics and determination of hemodynamically significant stenosis. Detailed evaluation of plaque morphology and identification of high-risk plaque features by CCTA have been confirmed as predictors of future outcomes, identifying patients at risk for ACSs. With quantitative coronary plaque assessment, the progression of the CAD or the response to therapy could be monitored by CCTA. The aim of this article is to review the future directions of emerging applications in CCTA, such as computed tomography (CT)-fractional flow reserve, imaging of vulnerable plaque features, and quantitative plaque imaging. We will also briefly discuss novel methods appearing in the coronary imaging scenario, such as machine learning, radiomics, and spectral CT.
