Coronary artery ectasia (CAE) is a well recognized clinical entity encountered during diagnostic cardiac catheterization. The etiopathogenesis of this condition is poorly understood. Due to the frequent presence of associated obstructive coronary artery disease it is considered to be a maladaptive process of atherosclerosis. Based on its association with aortic aneurysm, coronary ectasia is considered to be caused by genetic abnormalities. It is usually not a benign condition, as normal smooth laminar flow is disrupted with a potential of thrombus formation. The role of long-term anticoagulation in this condition has not been well established. It is speculated that with increasing use of newer, noninvasive modalities the incidence of ectasia may rise, therefore necessitating this review.
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http://dx.doi.org/10.1002/clc.20002 | DOI Listing |
Eur J Radiol
January 2025
Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167 Mannheim, Germany. Electronic address:
Objectives: Coronary CT angiography (CCTA) is an excellent tool in ruling out coronary artery disease (CAD) but tends to overestimate especially highly calcified plaques. To reduce diagnostic invasive catheter angiographies (ICA), current guidelines recommend CT-FFR to determine the hemodynamic significance of coronary artery stenosis. Photon-Counting Detector CT (PCCT) revolutionized CCTA and may improve CT-FFR analysis in guiding patients.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
January 2025
National Heart Center Singapore, Singapore, Singapore.
Aims: To identify differences in CT-derived perivascular (PVAT) and epicardial adipose tissue (EAT) characteristics that may indicate inflammatory status differences between post-treatment acute myocardial infarction (AMI) and stable coronary artery disease (CAD) patients.
Methods And Results: A cohort of 205 post-AMI patients (age 59.8±9.
PLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFCoron Artery Dis
January 2025
Department of Cardiology and Electrotherapy, Silesian Center for Heart Diseases.
Eur J Prev Cardiol
January 2025
Department of Clinical Sciences and Community Health, University of Milan, Via Commenda 19, Milan 20122, Italy.
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