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<p>Image registration has become an indispensable tool in medical diagnosis and intervention. The increasing need for speed and accuracy in clinical applications have motivated researchers to focus on developing fast and reliable registration algorithms. In particular, advanced deformable registration routines are emerging for medical applications involving soft-tissue organs such as brain, breast, kidney, liver, prostate, etc. Computational complexity of such algorithms are significantly higher than those of conventional rigid and affine methods, leading to substantial increases in execution time. In this thesis, we present a parallel implementation of a newly developed deformable image registration algorithm by Marami et al. [1] using the Computer Unified Device Architecture (CUDA). The focus of this study is on acceleration of the computations on a Graphics Processing Unit (GPU) to reduce the execution time to nearly real-time for diagnostic and interventional applications. The algorithm co-registers preoperative and intraoperative 3-dimensional magnetic resonance (MR) images of a deforming organ. It employs a linear elastic dynamic finite-element model of the deformation and distance measures such as mutual information and sum of squared difference to align volumetric image data sets. In this study, we report a parallel implementation of the algorithm for 3D-3D MR registration based on SSD on a CUDA capable NVIDIA GTX 480 GPU. Computationally expensive tasks such as interpolation, displacement and force calculation are significantly accelerated using the GPU. The result of the experiments carried out with a realistic breast phantom tissue shows a 37-fold speedup for the GPUbased implementation compared with an optimized CPU-based implementation in high resolution MR image registration. The CPU is a 3.20 GHz Intel core i5 650 processor with 4GB RAM that also hosts the GTX 480 GPU. This GPU has 15 streaming multiprocessors, each with 32 streaming processors, i.e. a total of 480 cores. The GPU implementation registers 3D-3D high resolution (512×512×136) image sets in just over 2 seconds, compared to 1.38 and 23.25 minutes for CPU and MATLAB-based implementations, respectively. Most GPU kernels which are employed in 3D-3D registration algorithm also can be employed to accelerate the 2D-3D registration algorithm in [1].</p> / Master of Applied Science (MASc)

This thesis outlines the development of an automatic image registration algorithm for matching 3D CT data to 2D fluoroscope X-ray images. The registration is required in order to calculate a transformation for measurements in the 2D image into the 3D representation. The algorithm achieves the registration by generating digitally reconstructed radiographs from the CT data set. The radiographs are 2D projection images, and therefore may be compared with the 2D Fluoroscope images. The X-ray and fluoroscope images were compared using the photometric-based registration algorithm, pseudocorrelation, with X^2 as the distance metric. An automated search algorithm was implemented using the Downhill Simplex of Nelder and Meade. The algorithm was successful in locating the position and orientation of the CT data set for calculating a digitally reconstructed radiograph to match the fluoroscope image. The CT data set was located with a maximum mean position error of 2.4 mm in xy, 4.4 mm in z, and xyz axial rotation within 0.5°. The standard deviation given 1800 random starting locations was 9.3 mm in x, 12.7 mm in y, 16.9 mm in z, xz axial rotation 2.5°, and y axial rotation of 1.9°. The search algorithm was successful in handling gross misalignment, however there were difficulties in convergence once within the vicinity of the global minimum. It is suggested to implement a hybrid search technique, switching to a conjugate gradient search algorithm once in the vicinity of the global minimum. An additional refinement would be a possible change of the distant metric, or the registration algorithm, once within the vicinity of the global minimum. Additional investigation needs to be directed towards testing the algorithm with live fluoroscope and CT data. This is required in order to assess registration performance when comparing different imaging modalities. / Thesis / Master of Engineering (ME)

This work presents two novel methods of estimating the state of a dynamic system in a Kalman Filtering framework. The first is an application specific method for use with systems performing Visual Odometry in a mostly planar scene. Because a Visual Odometry method inherently provides relative information about the pose of a platform, we use this system as part of the time update in a Kalman Filtering framework, and develop a novel way to propagate the uncertainty of the pose through this time update method. Our initial results show that this method is able to reduce localization error significantly with respect to pure INS time update, limiting drift in our test system to around 30 meters for tens of seconds. The second key contribution of this work is the Manifold EKF, a generalized version of the Extended Kalman Filter which is explicitly designed to estimate manifold-valued states. This filter works for a large number of commonly useful manifolds, and may have applications to other manifolds as well. In our tests, the Manifold EKF demonstrated significant advantages in terms of consistency when compared to other filtering methods. We feel that these promising initial results merit further study of the Manifold EKF, related filters, and their properties.

Cílem předkládané diplomové práce je pomocí lícování obrazů přesně popsat distorzní pole pro následné odstranění geometrické distorze. Snímky zkreslené geometrickou distorzí pochází z prozařovacího elektronového mikroskopu. První část práce se zabývá zejména teorií spojenou s elektronovou mikroskopií, vznikem geometrické distorze a samotnou obrazovou registrací s důrazem na intenzitní flexibilní metody lícování. Ve druhé části je pak představena vytvořená metoda pro modelování geometrické distorze a lícování obrazů postižených slabou geometrickou distorzí. Vyvinutá metoda je následně otestována na testovacích i reálných datech a srovnána s existujícími popsanými metodami pro obrazovou registraci (například open-source softwarem Elastix).

Medical image analysis aims at developing techniques to extract information from medical images. Among its many sub-fields, image registration and segmentation are two important topics. In this report, we present four pieces of work, addressing different problems as well as coupling them into a unified framework of shape based image segmentation. Specifically: 1. We link the image registration with the point set registration, and propose a globally optimal diffeomorphic registration technique for point set registration. 2. We propose an image segmentation technique which incorporates the robust statistics of the image and the multiple contour evolution. Therefore, the method is able to simultaneously extract multiple targets from the image. 3. By combining the image registration, statistical learning, and image segmentation, we perform a shape based method which not only utilizes the image information but also the shape knowledge. 4. A multi-scale shape representation based on the wavelet transformation is proposed. In particular, the shape is represented by wavelet coefficients in a hierarchical way in order to decompose the shape variance in multiple scales. Furthermore, the statistical shape learning and shape based segmentation is performed under such multi-scale shape representation framework.

Deep brain stimulation (DBS) surgery, a type of microelectrode-guided surgery, is an effective treatment for the movement disorders patients that can no longer be treated by medications. New rigid and non-rigid image registration methods were developed for the movement disorders patients that underwent DBS surgery. These new methods help study and analyze the brain shift during the DBS surgery and perform atlas-based segmentation of the deep brain structures for the DBS surgery planning and navigation. A diploë based rigid registration method for the intra-operative brain shift analysis during the DBS surgery was developed. The proposed method for the brain shift analysis ensures rigid registration based on diploë only, which can be treated as a rigid structure as opposed to the brain tissues. The results show that the brain shift during the DBS surgery is comparable to the size of the DBS targets and should not be neglected. This brain shift may further lengthen and complicate the DBS surgery contrary to the common belief that brain shift during the DBS surgery is not considerable. We also developed an integrated electrophysiological and anatomical atlas with eleven deep brain structures segmented by an expert, and electrophysiological data of four implant locations obtained from post-op MRI data of twenty patients that underwent DBS surgery. This atlas MR image is then non-rigidly registered with the pre-operative patient MR image, which provides initial DBS target location along with the segmented deep brain structures that can be used for guidance during the microelectrode mapping of the stereotactic procedure. The atlas based approach predicts the target automatically as opposed to the manual selection currently used. The results showed that 85% of the times, this automatic selection of the target location was closer to the target when compared to currently used technique.