Browsing by Subject "Cap stress"
Now showing 1 - 1 of 1
- Results Per Page
- Sort Options
Item Noninvasive assessment of coronary artery plaque vulnerability using computational solid mechanics(2024-05) Johnson, Michael James, Ph. D. ; Hughes, Thomas J. R.Coronary Artery Disease (CAD) is a leading cause of death worldwide. It is caused by the buildup of atherosclerotic plaque in the coronary arteries. Over time these plaques may rupture, leading to an Acute Coronary Syndrome (ACS) such as heart attack and sudden cardiac death. Due to this risk, the ability to identify so-called "vulnerable plaques" that have a high risk of rupture is essential in developing effective strategies for the diagnosis, prevention, and treatment of CAD. Recent advances in imaging technology and deep learning image segmentation have enabled the application of computational medicine to overcome the current limitations of conventional approaches to risk assessment. It is now possible to construct detailed patient-specific models of the heart from noninvasive Coronary Computed Tomography Angiography (CCTA) that can be used for biomechanical assessment of plaque vulnerability in order to predict rupture and ACS. Previous studies have indicated that high stresses in the fibrous cap are associated with increased risk of rupture. In this dissertation, I identify the key contributing factors of high stresses in the fibrous cap in order to gain a biomechanical understanding of vulnerable plaques. Then, I develop a patient-specific biomechanical model using noninvasive CCTA and computational solid mechanics that is used for stress analysis of the fibrous cap. Finally, I use the model in a retrospective study of 209 lesions to identify biomechanical predictors of ACS and evaluate the potential clinical utility of biomechanical assessment of coronary lesions. The primary conclusions of this dissertation are (i) biomechanics plays a key role in the prediction of plaque rupture and ACS, and (ii) noninvasive estimates of stresses in the fibrous cap have potential as predictive metrics.