Coronary artery disease(CAD) is one of the leading causes of mortality and morbidity worldwide. Clinically, it refers to atherosclerotic changes in the coronary arteries and is usually assessed... Show moreCoronary artery disease(CAD) is one of the leading causes of mortality and morbidity worldwide. Clinically, it refers to atherosclerotic changes in the coronary arteries and is usually assessed with a stress electrocardiogram and conventional coronary angiography(CCA). CCA, however, is an invasive technique and carries a small risk of complications. Non-invasive techniques such as coronary angiography with CT(CTCA), and myocardial perfusion imaging (MPI) with SPECT and MR are therefore used as gatekeeper tests before CCA. These techniques provide valuable information on both the coronary stenoses and their hemodynamic impact on the myocardial function. However, each of these techniques presents only one aspect of CAD. To achieve a higher level of accuracy and precision in CAD assessment, integration of information from different cardiac imaging modalities is essential. The goal of this thesis was therefore to develop techniques to realize this multimodal diagnostic image integration to enhance CAD diagnosis. To this end, we developed novel algorithms for near automated analysis of magnetic resonance based myocardial perfusion images. In addition, we developed and evaluated a new integration framework that allows comprehensive visualization of physiologic information from myocardial perfusion imaging -either with MR or SPECT and anatomical information from CTCA Show less
