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

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

Groupwise image registration of multimodal head-and-neck images

\u3cp\u3eFusion of multimodal medical images using deformable registration is of high interest for head-and-neck tumour treatment planning. In this context, more than two images often have to be aligned for a given patient. The conventional, pairwise way to register multiple images is to select one of them as fixed reference and independently align each remaining image with it. An alternative method would be to simultaneously register the images using a groupwise registration scheme, thus eliminating the need to select a reference image and avoiding any bias due to this arbitrary choice. In this study, we propose a novel groupwise image registration technique, combining a principal component analysis (PCA) based similarity metric and modality independent neighbourhood descriptors (MIND). Results on 16 patients show that the images are slightly better aligned when using the proposed registration method than when using pairwise registration.\u3c/p\u3