Non-rigid Groupwise Image Registration for Motion Compensation in Quantitative MRI

International audienc

Application of Quantitative MRI Techniques in Ischemic and Congenital Heart Disease image-guided therapy

It is estimated that each year, 30,000 people in the Netherlands suffer from myocardial infarction, and is in fact a major public health care burden. Next to ischaemic heart disease (IHD), thanks to modern medicine, more and more patients with congenital heart disease (CHD) reach adulthood, but nonetheless require lifelong professional care. Imaging techniques are essential in these two groups of patients for establishing diagnosis, guiding therapy and predicting outcomes. This thesis investigated the application of cardiac MRI in patients with IHD and CHD and evaluated novel non-invasive MRI techniques in both humans and in a porcine model. In this thesis we showed that cardiac MRI is an excellent tool for the evaluation of atherosclerotic and congenital cardiovascular disease. It is an excellent tool in predicting outcome after STEM! in patients undergoing primary percutaneous coronary intervention. Furthermore, studied in this thesis, the incidence of per procedural complications in patients with CHD seems higher than in the general population suggesting that placement of a pacemaker device might be challenging in this group of patients

Advanced Image Analysis for Modeling the Aging Brain

Both normal aging and neurodegenerative diseases such as Alzheimer’s disease (AD) cause morphological changes of the brain due to neurodegeneration. As neurodegeneration due to disease may be difficult to distinguish from that of normal aging, interpretation of magnetic resonance (MR) brain images in the context of diagnosis of neurodegenerative diseases is challenging, especially in the early stages of the disease. This thesis presented comprehensive models of the aging brain and novel computer-aided diagnosis methods, based on advanced, quantitative analysis of brain MR images, facilitating the differentiation between normal and abnormal neurodegeneration. I aimed to evaluate and develop methods for clinical decision support using features derived from MR brain images: I evaluated a classification method to predict global cognitive decline in the general population, evaluated five brain segmentation methods and developed a spatio-temporal model of morphological differences in the brain due to normal aging. To create this model I developed two novel techniques that allow performing non-rigid groupwise image registration on large imaging datasets. The novel aging brain models and computer-aided diagnosis methods facilitate the differentiation between normal and abnormal neurodegeneration. This will help in establishing more accurate diagnoses of patients, and in identifying patients at risk of developing neurodegenerative disease before symptoms emerge. In the future, the method’s performance and efficacy should be evaluated in clinical practice

Advanced Medical Image Registration Methods for Quantitative Imaging and Multi-Channel Images

This thesis proposes advanced medical image registration methods for applications that can be grouped in two broad themes. The first theme focuses on registration techniques increasing the reliability of _quantitative measurements_ extracted from sets of medical images. The second theme that is considered in this thesis is the registration of _multi-channel_ images