Advancements in cardiovascular imaging: serial coronary CT and myocardial CT perfusion quantification techniques

Abstract

This thesis explores the role of cardiovascular imaging and demonstrates newly developed methods for (serial) coronary atherosclerosis detection and quantification of ischemia and left ventricle (LV) dimensions.

Today, assessment of coronary atherosclerosis is done using coronary computed tomography angiography (CCTA) at one moment in time. However, when using CCTA in a serial manner one may be able to assess the progression of atherosclerosis over time. As such, patient specific cut-off values were developed based on the scan quality in order to create one 3D image of coronary plaque differences. This is especially useful as it makes intermittent assessment of a baseline and follow-up scan obsolete. Furthermore, thorough analysis of serial CCTA has demonstrated that quantitative plaque features are more predictive of major cardiac events (MACE) and plaque progression as opposed to qualitative plaque features.

Ultimately, degree of coronary artery stenosis due to atherosclerosis is predictive of myocardial ischemia. In this thesis, a novel method for quantifying myocardial ischemia is described allowing for quantitative correlation of the latter to its subsequent coronary artery stenosis.

In contrast, non-ischemic heart disease may cause changes in LV dimensions. Magnetic resonance imaging (MRI) is still regarded the gold standard for quantifying LV dimensions. In this thesis, use of AI has been proven useful in LV dimension using CCTA, offering a solid alternative to MRI.