Algorithms for the analysis of 3D magnetic resonance angiography images
Résumé
Atherosclerosis is a disease of the arterial wall, progressively impairing blood flow as it spreads throughout the body. The heart attacks and strokes that result of this condition cause more deaths than cancer in industrial countries. Angiography refers to the group of imaging techniques used through the diagnosis, treatment planning and follow-up of atherosclerosis. In recent years, Magnetic Resonance Angiography (MRA) has shown promising abilities to supplant conventional, invasive, X-ray-based angiography. In order to fully benefit from this modality, there is a need for more objective and reproducible methods. This thesis shows, in two applications, how computerized image analysis can help define and implement these methods. First, by using segmentation to improve visualization of blood-pool contrast enhanced (CE)-MRA, with an additional application in coronary Computerized Tomographic Angiography. We show that, using a limited amount of user interaction and an algorithmic framework borrowed from graph theory and fuzzy logic theory, we can simplify the display of complex 3D structures like vessels. Second, by proposing a methodology to analyze the geometry of arteries in whole-body CE-MRA. The vessel centreline is extracted, and geometrical properties of this 3D curve are measured, to improve interpretation of the angiograms. It represents a more global approach than the conventional evaluation of atherosclerosis, as a first step towards screening for vascular diseases. We have developed the methods presented in this thesis with clinical practice in mind. However, they have the potential to be useful to other applications of computerized image analysis.