Diffeomorphic Metric Mapping and Probabilistic Atlas Generation of Hybrid Diffusion Imaging based on BFOR Signal Basis

We propose a large deformation diffeomorphic metric mapping algorithm to align multiple b-value diffusion weighted imaging (mDWI) data, specifically acquired via hybrid diffusion imaging (HYDI), denoted as LDDMM-HYDI. We then propose a Bayesian model for estimating the white matter atlas from HYDIs. We adopt the work given in Hosseinbor et al. (2012) and represent the q-space diffusion signal with the Bessel Fourier orientation reconstruction (BFOR) signal basis. The BFOR framework provides the representation of mDWI in the q-space and thus reduces memory requirement. In addition, since the BFOR signal basis is orthonormal, the L2 norm that quantifies the differences in the q-space signals of any two mDWI datasets can be easily computed as the sum of the squared differences in the BFOR expansion coefficients. In this work, we show that the reorientation of the $q$-space signal due to spatial transformation can be easily defined on the BFOR signal basis. We incorporate the BFOR signal basis into the LDDMM framework and derive the gradient descent algorithm for LDDMM-HYDI with explicit orientation optimization. Additionally, we extend the previous Bayesian atlas estimation framework for scalar-valued images to HYDIs and derive the expectation-maximization algorithm for solving the HYDI atlas estimation problem. Using real HYDI datasets, we show the Bayesian model generates the white matter atlas with anatomical details. Moreover, we show that it is important to consider the variation of mDWI reorientation due to a small change in diffeomorphic transformation in the LDDMM-HYDI optimization and to incorporate the full information of HYDI for aligning mDWI

Left-Invariant Diffusion on the Motion Group in terms of the Irreducible Representations of SO(3)

In this work we study the formulation of convection/diffusion equations on the 3D motion group SE(3) in terms of the irreducible representations of SO(3). Therefore, the left-invariant vector-fields on SE(3) are expressed as linear operators, that are differential forms in the translation coordinate and algebraic in the rotation. In the context of 3D image processing this approach avoids the explicit discretization of SO(3) or $S_2$, respectively. This is particular important for SO(3), where a direct discretization is infeasible due to the enormous memory consumption. We show two applications of the framework: one in the context of diffusion-weighted magnetic resonance imaging and one in the context of object detection

Determining the Dependence Structure of Multivariate Extremes

In multivariate extreme value analysis, the nature of the extremal dependence between variables should be considered when selecting appropriate statistical models. Interest often lies with determining which subsets of variables can take their largest values simultaneously, while the others are of smaller order. Our approach to this problem exploits hidden regular variation properties on a collection of non-standard cones and provides a new set of indices that reveal aspects of the extremal dependence structure not available through existing measures of dependence. We derive theoretical properties of these indices, demonstrate their value through a series of examples, and develop methods of inference that also estimate the proportion of extremal mass associated with each cone. We apply the methods to UK river flows, estimating the probabilities of different subsets of sites being large simultaneously

Medical Image Registration Using Deep Neural Networks

Registration is a fundamental problem in medical image analysis wherein images are transformed spatially to align corresponding anatomical structures in each image. Recently, the development of learning-based methods, which exploit deep neural networks and can outperform classical iterative methods, has received considerable interest from the research community. This interest is due in part to the substantially reduced computational requirements that learning-based methods have during inference, which makes them particularly well-suited to real-time registration applications. Despite these successes, learning-based methods can perform poorly when applied to images from different modalities where intensity characteristics can vary greatly, such as in magnetic resonance and ultrasound imaging. Moreover, registration performance is often demonstrated on well-curated datasets, closely matching the distribution of the training data. This makes it difficult to determine whether demonstrated performance accurately represents the generalization and robustness required for clinical use. This thesis presents learning-based methods which address the aforementioned difficulties by utilizing intuitive point-set-based representations, user interaction and meta-learning-based training strategies. Primarily, this is demonstrated with a focus on the non-rigid registration of 3D magnetic resonance imaging to sparse 2D transrectal ultrasound images to assist in the delivery of targeted prostate biopsies. While conventional systematic prostate biopsy methods can require many samples to be taken to confidently produce a diagnosis, tumor-targeted approaches have shown improved patient, diagnostic, and disease management outcomes with fewer samples. However, the available intraoperative transrectal ultrasound imaging alone is insufficient for accurate targeted guidance. As such, this exemplar application is used to illustrate the effectiveness of sparse, interactively-acquired ultrasound imaging for real-time, interventional registration. The presented methods are found to improve registration accuracy, relative to state-of-the-art, with substantially lower computation time and require a fraction of the data at inference. As a result, these methods are particularly attractive given their potential for real-time registration in interventional applications

Patient-specific modellling of cortical spreading depression applied to migraine studies.

254 p.-La migraña es un trastorno neurológico muy común. Un tercio de los pacientes que sufren migraña experimentan lo que se denomina aura, una serie de alteraciones sensoriales que preceden al típico dolor de cabeza unilateral. Diversos estudios apuntan a la existencia de una correlación entre el aura visual y la depresión cortical propagada (DCP), una onda de despolarización que tiene su origen en el córtex visual para propagarse, a continuación, por todo el córtex hacia las zonas periféricas. La complejidad y la elevada especificidad de las características del córtex cerebral sugieren que la geometría podría tener un impacto significativo en la propagación de la DCP. En esta tesis hemos combinado dos modelos existentes: un modelo neurológico pormenorizado para el componente electrofisiológico de la DCP y un modelo de reacción-difusión que tiene en consideración la difusión del potasio, el impulsor de la propagación de la DCP. Durante el proceso, hemos integrado dos aspectos de la DCP que tienen lugar en diferentes escalas de tiempo: la dinámica electrofisiológica seguiría un patrón temporal del orden de milisegundos, mientras que la dinámica del potasio extracelular que acciona las funciones de propagación de la DCP se mediría en una escala de minutos. Como resultado, obtendremos un modelo multiescalar EDP-EDO. Asimismo, hemos incorporado los datos específicos del paciente en el modelo DCP: (i) la geometría cerebral específica de un paciente obtenida a través de resonancia magnética, y (ii) los tensores de conductividad personalizados obtenidos a través de diffusion tensor images. A fin de estudiar el papel que desempeña la geometría en la propagación de la DCP, hemos definido las cantidades de interés (CdI) relacionadas con la geometría y las que dependen de la DCP y las hemos evaluado en dos casos prácticos. Si bien la geometría no parece tener un impacto significativo en la propagación de la DCP, algunas CdI han resultado ser unas candidatas muy prometedoras para facilitar la clasificación de individuos sanos y pacientes con migraña. Finalmente, para justificar la carencia de datos experimentales para la validación y selección de los parámetros del modelo, hemos aplicado diversas técnicas de cuantificación de la incertidumbre al modelo DCP y hemos analizado el impacto de las diversas elecciones de parámetros en el resultado del modelo.Migraine is a common neurological disorder and one-third of migraine patients suffer from migraine aura, a perceptual disturbance preceding the typically unilateral headache. Cortical spreading depression (CSD), a depolarisation wave that originates in the visual cortex and propagates across the cortex to the peripheral areas, has been suggested as a correlate of visual aura by several studies. The complex and highly individual-specific characteristics of the brain cortex suggest that the geometry might have a significant impact on CSD propagation. In this thesis, we combine two existing models, a detailed neurological model for the electrophysiological component of CSD and a reaction-diffusion model accounting for the potassium diffusion, the driving force of CSD propagation. In the process, we integrate two aspects of CSD that occur at different time scales: the electrophysiological dynamics features a temporal scale in the order of milliseconds, while the extracellular potassium dynamics that triggers CSD propagation features is on the scale of minutes. As a result we obtain a multi-scale PDE-ODE model. In addition, we incorporate patient-specific data in the CSD model: (i) a patient-specific brain geometry obtained from magnetic resonance imaging, and (ii) personalised conductivity tensors derived from diffusion tensor imaging data. To study the role of the geometry in CSD propagation, we define geometric and CSD-dependent quantities of interest (QoI) that we evaluate in two case studies. Even though the geometry does not seem to have a major impact on the CSD propagation, some QoI are promising candidates to aid in the classification of healthy individuals and migraine patients. Finally, to account for the lack of experimental data for validation and selection of the model parameters, we apply different techniques of uncertainty quantification to the CSD model and analyse the impact of various parameter choices on the model outcom

Investigation of new learning methods for visual recognition

Visual recognition is one of the most difficult and prevailing problems in computer vision and pattern recognition due to the challenges in understanding the semantics and contents of digital images. Two major components of a visual recognition system are discriminatory feature representation and efficient and accurate pattern classification. This dissertation therefore focuses on developing new learning methods for visual recognition. Based on the conventional sparse representation, which shows its robustness for visual recognition problems, a series of new methods is proposed. Specifically, first, a new locally linear K nearest neighbor method, or LLK method, is presented. The LLK method derives a new representation, which is an approximation to the ideal representation, by optimizing an objective function based on a host of criteria for sparsity, locality, and reconstruction. The novel representation is further processed by two new classifiers, namely, an LLK based classifier (LLKc) and a locally linear nearest mean based classifier (LLNc), for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Second, a new generative and discriminative sparse representation (GDSR) method is proposed by taking advantage of both a coarse modeling of the generative information and a modeling of the discriminative information. The proposed GDSR method integrates two new criteria, namely, a discriminative criterion and a generative criterion, into the conventional sparse representation criterion. A new generative and discriminative sparse representation based classification (GDSRc) method is then presented based on the derived new representation. Finally, a new Score space based multiple Metric Learning (SML) method is presented for a challenging visual recognition application, namely, recognizing kinship relations or kinship verification. The proposed SML method, which goes beyond the conventional Mahalanobis distance metric learning, not only learns the distance metric but also models the generative process of features by taking advantage of the score space. The SML method is optimized by solving a constrained, non-negative, and weighted variant of the sparse representation problem. To assess the feasibility of the proposed new learning methods, several visual recognition tasks, such as face recognition, scene recognition, object recognition, computational fine art analysis, action recognition, fine grained recognition, as well as kinship verification are applied. The experimental results show that the proposed new learning methods achieve better performance than the other popular methods