Understanding the mechanisms that regulate atherosclerotic plaque formation and evolution is a crucial step for developing treatment strategies that will prevent plaque progression and reduce cardiovascular events. Advances in signal processing and the miniaturization of medical devices have enabled the design of multimodality intravascular imaging catheters that allow complete and detailed assessment of plaque morphology and biology. However, a significant limitation of these novel imaging catheters is that they provide two-dimensional (2D) visualization of the lumen and vessel wall and thus they cannot portray vessel geometry and 3D lesion architecture. To address this limitation computer-based methodologies and user-friendly software have been developed. These are able to off-line process and fuse intravascular imaging data with X-ray or computed tomography coronary angiography (CTCA) to reconstruct coronary artery anatomy. The aim of this review article is to summarize the evolution in the field of coronary artery modeling; we thus present the first methodologies that were developed to model vessel geometry, highlight the modifications introduced in revised methods to overcome the limitations of the first approaches and discuss the challenges that need to be addressed, so these techniques can have broad application in clinical practice and research.
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http://dx.doi.org/10.3389/fcvm.2020.00033 | 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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