Efficient GPU-based Image Registration : for Detailed Large-Scale Whole-body Analysis

Imaging has become an important aspect of medicine, enabling visualization of internals in a non-invasive manner. The rapid advancement and adoption of imaging techniques have led to a demand for tools able to take advantage of the information that is produced. Medical image analysis aims to extract relevant information from acquired images to aid diagnostics in healthcare and increase the understanding within medical research. The main subject of this thesis, image registration, is a widely used tool in image analysis that can be employed to find a spatial transformation aligning a set of images. One application, that is described in detail in this thesis, is the use of image registration for large-scale analysis of whole-body images through the utilization of the correspondences defined by the resulting transformations. To produce detailed results, the correspondences, i.e. transformations, need to be of high resolution and the quality of the result has a direct impact on the quality of the analysis. Also, this type of application aims to analyze large cohorts and the value of a registration method is not only weighted by its ability to produce an accurate result but also by its efficiency. This thesis presents two contributions on the subject; a new method for efficient image registration with the ability to produce dense deformable transformations, and the application of the presented method in large-scale analysis of a whole-body dataset acquired using an integrated positron emission tomography (PET) and magnetic resonance imaging (MRI) system. In this thesis, it is shown that efficient and detailed image registration can be performed by employing graph cuts and a heuristic where the optimization is performed on subregions of the image. The performance can be improved further by the efficient utilization of a graphics processing unit (GPU). It is also shown that the method can be employed to produce a model on health based on a PET-MRI dataset which can be utilized to automatically detect pathology in the imaging