Nonrigid medical image registration

Over the last two decades medical image processing has become a fundamental tool in medicine, and brain imaging is a paradigm thereof. The fast development of the imaging acquisition techniques allows medical staff to dispose of a variety of data coming from different imaging modalities. However, the analysis of such amount of data based on visual inspection is very difficult and highly time consuming. Often, the information contained in a medical image cannot be entirely seen by a human eye and vice-versa, computers do not have human common sense or the experience acquired by medical experts. Therefore, it is desirable to combine both medical experts and computers in an optimal trade-off to improve the outcome of the wide range of applications where medical imaging is nowadays applied. Among these applications we can mention diagnosis, surgical planning and intra-operative surgical navigation. Image registration is one of the most popular image processing techniques used in clinical environments. It consists of bringing two or more images into a single spatial representation, i.e. finding the optimal geometric transformation between the data. Image registration overcomes the difficulties encountered by clinicians to fuse images mentally since it enables to integrate different images into one representation. The applications of image registration in medicine are numerous and can be grouped in clinical, related to detection and diagnosis, and surgical. Some of these applications are: preprocedural planning and simulation, interventional radiology, diagnostic radiology, minimally invasive procedures, radiation therapy, intraoperative navigation, robot-assisted interventions, etc. During the decade of the 90s a large number of registration algorithms were proposed, linked to a variety of medical applications. Nowadays, rigid registration is considered to be a well-covered domain with efficient solutions for the most common applications. Current research mostly focuses on nonrigid registration methods. However, the complex framework of nonrigid registration makes it a complex problem where there is not a universal solution. This complex framework includes a number of components that interact: the dimensionality of the data involved, the feature space to base the registration on, the kind and domain of the transformation model, the modalities of the data and the subjects involved, the user interaction, the similarity metric to assess the alignment quantitatively, the search space where the optimal solution has to be found, and the search strategy including optimization of the objective function and implementation details. We can summarize the motivation of this thesis in the following sentence: Validation of nonrigid registration algorithms is a very difficult task and rather an application-dependent problem. Therefore, this dissertation is structured to face the problem of brain image registration in three closely correlated parts: algorithms, applications and validation. In the algorithmic context, we propose a control point based nonrigid registration algorithm which aims to improve the performance of classical point based algorithms. In the framework of the implementation of the algorithm we develop a control point detector based on information theory tools and a transformation model based on a type of radial basis functions specially suited for interpolation of scattered and nonuniform distributed data samples. The algorithm is evaluated for typical inter-subject monomodal and multimodal brain image registration. In the context of clinical applications we face a neurosurgical application: the subthalamic nucleus targeting for deep brain stimulation in Parkinson's disease surgery. The extensive study covers from state-of-the-art targeting procedures to automatic targeting methods through different registration algorithms. Moreover, we analyze the influence of surrounding anatomical features on the target location. We also study the feasibility of an atlas of landmarks aiming to improve the targeting accuracy and at the same time to reduce both the computational cost and the preprocessing needed compared to the other automatic targeting methods proposed. All the proposed methods and techniques are statistically cross-validated against state-of-the-art techniques and expert's targeting variability through an application-adapted validation scheme. The results show that automatic subthalamic nucleus localization is possible and as accurate as the methods currently used. We also provide valuable conclusions about the anatomical structures that must be used to infer more accurate targets. In the context of validation of brain image registration, we develop a general (non application-oriented) validation scheme that provides an independent and high dimensional means of assessing the quality of the alignment given by a registration algorithm. This procedure is applied to structural magnetic resonance images and is based on the information carried by the diffusion tensor images. It provides an error measure per voxel allowing an independent evaluation for different regions. Almost 800 registered images are analyzed to provide with strength the conclusions derived from the statistical analysis.