Computational methods for medical image registration

Abstract

A significant amount of research has been dedicated to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges remain when processing and analyzing information from medical images. Image registration is a technique to find a reasonable transformation that best aligns a pair or group of images. Of particular interest in this thesis is the use of image registration in three main categories: cardiac fiber atlas construction from healthy porcine hearts, motion correction in contrast-enhanced image sequences, and the development of novel computational techniques that improve the performance of existing medical image registration methods. We provide an overview of each of the problems that we tackled, followed by a discussion of the underlying motivation and theory behind the proposed methods, and extensive validations. Most importantly, this work highlights the central role that image registration plays in biomedical research - from producing clinically relevant image-based predictive models, to enabling accurate diagnosis of diseases and the analysis of treatment response.