• Journal of the Korean Society of Radiology
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• v.83 no.1
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• pp.3-27
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• 2022

Cardiac CT has been proven to provide diagnostic and prognostic evaluation of coronary artery disease for cardiovascular risk stratification and treatment decision-making based on rapid technological development and various research evidence. Coronary CT angiography has emerged as a gateway test for coronary artery disease that can reduce invasive angiography due to its high negative predictive value, but the diagnostic specificity is relatively low. However, coronary CT angiography is likely to overcome its limitations through functional evaluation to identify the hemodynamic significance of coronary artery disease by analyzing myocardial perfusion and fractional flow reserve through cardiac CT. Recently, studies have been actively conducted to incorporate artificial intelligence to make this more objective and reproducible. In this review, functional imaging techniques of cardiac computerized tomography are explored.

• Journal of the Korean Society of Radiology
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• v.81 no.2
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• pp.250-271
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• 2020

Assessment of myocardial ischemia in patients with stable angina is important in deciding whether to treat coronary artery disease and in predicting clinical outcome. The fractional flow reserve is a standard reference for the diagnosis of myocardial ischemia, but this procedure has limitations because of its invasiveness. Coronary computed tomography angiography (CCTA) is now an established tool in the anatomic diagnosis of coronary artery disease; however, there are limits to the diagnosis of hemodynamically important stenosis that causes myocardial ischemia. In order to address this problem, studies using quantification of coronary atherosclerotic plaques, myocardial perfusion, and noninvasive calculation of fractional flow reserve based on CCTA have been actively conducted and recognized for their diagnostic value. In this review, several imaging techniques of CCTA used to assess myocardial ischemia are described.

• Journal of KIISE
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• v.43 no.4
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• pp.419-429
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• 2016

Generating a 3D surface model from coronary artery segmentation helps to not only improve the rendering efficiency but also the diagnostic accuracy by providing physiological informations such as fractional flow reserve using computational fluid dynamics (CFD). This paper proposes a method to generate a triangular surface mesh using vessel structure information acquired with coronary artery segmentation. The marching cube algorithm is a typical method for generating a triangular surface mesh from a segmentation result as bit mask. But it is difficult for methods based on marching cube algorithm to express the lumen of thin, small and winding vessels because the algorithm only works in a three-dimensional (3D) discrete space. The proposed method generates a more accurate triangular surface mesh for each singular vessel using vessel centerlines, normal vectors and lumen diameters estimated during the process of coronary artery segmentation as the input. Then, the meshes that are overlapped due to branching are processed by mesh merging and merged into a coronary mesh.