Patient-specific 3D coronary model in cardiac catheterisation laboratories.

Front Cardiovasc Med

Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.

Published: July 2024


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Article Abstract

Coronary artery disease is caused by the buildup of atherosclerotic plaque in the coronary arteries, affecting the blood supply to the heart, one of the leading causes of death around the world. X-ray coronary angiography is the most common procedure for diagnosing coronary artery disease, which uses contrast material and x-rays to observe vascular lesions. With this type of procedure, blood flow in coronary arteries is viewed in real-time, making it possible to detect stenoses precisely and control percutaneous coronary interventions and stent insertions. Angiograms of coronary arteries are used to plan the necessary revascularisation procedures based on the calculation of occlusions and the affected segments. However, their interpretation in cardiac catheterisation laboratories presently relies on sequentially evaluating multiple 2D image projections, which limits measuring lesion severity, identifying the true shape of vessels, and analysing quantitative data. modelling, which involves computational simulations of patient-specific data, can revolutionise interventional cardiology by providing valuable insights and optimising treatment methods. This paper explores the challenges and future directions associated with applying patient-specific models in catheterisation laboratories. We discuss the implications of the lack of patient-specific models and how their absence hinders the ability to accurately predict and assess the behaviour of individual patients during interventional procedures. Then, we introduce the different components of a typical patient-specific model and explore the potential future directions to bridge this gap and promote the development and utilisation of patient-specific models in the catheterisation laboratories.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11257904PMC
http://dx.doi.org/10.3389/fcvm.2024.1398290DOI Listing

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