Dissertation Defense of Darko Zikic

Title: Contributions to Medical Image Registration: Theoretical Insights and New Methodologies

Abstract:

Image registration, also known as image alignment, spatial normalization, or motion estimation, is the process of computing the spatial transformation between corresponding structures of objects depicted in two or more images. In medical scenarios, the spatial transformation between corresponding points can have various causes, such as natural patient movement, different patient positioning, or anatomical changes. Sometimes, the apparent misalignment is caused by the task of comparing images of different subjects. Thus, image registration is often the foundation for further analysis and applications, such as fusion of different modalities for diagnosis and interventions.

Image registration presents an active field of research, among other reasons also due to the complex nature of the associated optimization problem. Among the registration methods, the so called deformable registration problem, which treats high-dimensional transformation models, is particularly challenging. In the first part of this work, we present an overview of existing intensity-based registration methods, with focus on deformable approaches. Our aim is to point out the relations between the different approaches, and to highlight the common elements. In the second part of this thesis, we present several of our contributions to the field of registration of medical images, most of them affecting deformable registration methods:

1) We propose a simple and efficient preconditioning scheme for improvement of the convergence speed of gradient-based methods for arbitrary image-based difference measures in deformable registration. The proposed scheme is especially useful for deformable multi-modal registration employing statistical difference measures, since in these cases, the range of applicable efficient optimization schemes is strongly limited, due to the structure and size of the problem.

2) Alignment of angiographic 3D scans to 2D projections is an important issue for navigation during interventions. For the common single-view setting, in which only one 2D projection is available, we introduce a method for deformable 2D-3D registration of vascular structures. Prior to our work, methods employing a rigid transformation model presented the state of the art for this problem.

3) For linear registration, which is often performed prior to deformable registration, we discuss the use of Markov Random Field (MRF) modeling and discrete optimization. The approach is based on the approximation of the original energy, such that efficient discrete optimization becomes applicable. The resulting framework allows to transfer the advances from the active field of discrete optimization to linear registration.

4) For generation of statistical deformation models, we analyze the deformations resulting from deformable registration and find that they contain significant linear components. Based on this observation, we propose the use of minimal deformations, from which the linear transformation components are removed. We demonstrate that this step is non-optional for creation of accurate models representing the major deformations within a population.

Committee Head: Prof. Cremers, TUM
1st Referee: Prof. Navab, TUM
2nd Referee: Prof. Rueckert, Imperial College London

Date: Tuesday, December 20, 2011

Time: 17:00

Location: MI 02.09.023

Technische Universität München
Fakultät für Informatik
Boltzmannstr. 3
85748 Garching b. München
Germany