Total Hip Replacement (THR) has become a common surgical procedure in recent years, due to the increase in the aging population with hip osteoarthritis. Identifying the proper orientation of the pelvis is a critical step in accurate placement of the femur prosthesis in the acetabulum in THR. The general approach to localize the orientation of the pelvic anatomical coordinate system (PaCS) is to use intra-operative X-ray fluoroscopy in a specialized interventional radiology facility to guide the procedure. Employing intra-operative ultrasound (US) imaging fused with pre-operative CT scan or fluoroscopy imaging was proposed to eliminate the ionizing radiation of intra-operative X-ray to the patient and the need for radiology facilities in the OR. However, the use of pre-operative imaging exposes patients to accumulative ionizing radiation which is desirable to be eliminated. In this thesis, I propose to replace pre-operative imaging with a statistical shape model (SSM) of the pelvis which is constructed from CT images of patients. An automatic deformable registration of a pelvis anatomical shape model to a sparse set of 2D ultrasound images of the pelvis is presented in order to localize the PaCS. In this registration technique, a set of 2D slices are extracted from the pelvic shape model, based on the approximate location and orientation of a corresponding 2D ultrasound image. The comparison of the shape model slices and ultrasound images is made possible by using an ultrasound simulation technique and a correlation-based similarity metric. During the registration, an instance of the shape model is generated that best matches the ultrasound data. I demonstrate the feasibility of our proposed approach in localizing the PaCS on four patient phantoms and on data from two male human cadavers. None of the test data sets were included in the SSM generation. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2010-08-23 17:57:24.708

Traditionally, uncertainties are handled by expanding the irradiated volume to ensure target dose coverage to a certain probability. The uncertainties arise from e.g. the uncertainty in positioning of the patient at every fraction, organ motion and in defining the region of interests on the acquired images. The applied margins are inherently population based and do not exploit the geometry of the individual patient. Probabilistic planning on the other hand incorporates the uncertainties directly into the treatment optimization and therefore has more degrees of freedom to tailor the dose distribution to the individual patient. The aim of this thesis is to create a framework for probabilistic evaluation and optimization based on the concept of dose coverage probabilities. Several computational challenges for this purpose are addressed in this thesis. The accuracy of the fraction by fraction accumulated dose depends directly on the accuracy of the deformable image registration (DIR). Using the simulation framework, we could quantify the requirements on the DIR to 2 mm or less for a 3% uncertainty in the target dose coverage. Probabilistic planning is computationally intensive since many hundred treatments must be simulated for sufficient statistical accuracy in the calculated treatment outcome. A fast dose calculation algorithm was developed based on the perturbation of a pre-calculated dose distribution with the local ratio of the simulated treatment’s fluence and the fluence of the pre-calculated dose. A speedup factor of ~1000 compared to full dose calculation was achieved with near identical dose coverage probabilities for a prostate treatment. For some body sites, such as the cervix dataset in this work, organ motion must be included for realistic treatment simulation. A statistical shape model (SSM) based on principal component analysis (PCA) provided the samples of deformation. Seven eigenmodes from the PCA was sufficient to model the dosimetric impact of the interfraction deformation. A probabilistic optimization method was developed using constructs from risk management of stock portfolios that enabled the dose planner to request a target dose coverage probability. Probabilistic optimization was for the first time applied to dataset from cervical cancer patients where the SSM provided samples of deformation. The average dose coverage probability of all patients in the dataset was within 1% of the requested.

We present an image-based approach to infer 3D structure parameters using a probabilistic "shape+structure'' model. The 3D shape of a class of objects may be represented by sets of contours from silhouette views simultaneously observed from multiple calibrated cameras. Bayesian reconstructions of new shapes can then be estimated using a prior density constructed with a mixture model and probabilistic principal components analysis. We augment the shape model to incorporate structural features of interest; novel examples with missing structure parameters may then be reconstructed to obtain estimates of these parameters. Model matching and parameter inference are done entirely in the image domain and require no explicit 3D construction. Our shape model enables accurate estimation of structure despite segmentation errors or missing views in the input silhouettes, and works even with only a single input view. Using a dataset of thousands of pedestrian images generated from a synthetic model, we can perform accurate inference of the 3D locations of 19 joints on the body based on observed silhouette contours from real images.

With continually growing availability of high performance computing resources, the finite element methods (FEM) are becoming increasingly more efficient and practical research tools. In the domain of computational biomechanics, FEMs have been successfully applied in investigation of biomedical problems that include impact and fracture mechanics of bone, load transmission through the joints, feasibility of joint replacements, and many others. The present research study was concerned with the development of a detailed, anatomically accurate, finite element model of the human hand and wrist. As a first step in this direction, we used a publically available database of wrist bone anatomy and carpal kinematics to construct a multi-body statistical shape model (SSM) of the wrist. The resulting model provides an efficient parameterization of anatomical variations of the entire training set and can thus overcome the major shortcoming of conventional biomechanical models associated with limited generalization ability. The main contributions of this work are: 1) A robust method for constructing multi-body SSM of the wrist from surface meshes. 2) A novel technique for resampling closed genus-0 meshes to produce high quality triangulations suitable for finite element simulations. Additionally, all techniques developed in the course of this study could be directly applied to create an equivalent model of the tarsus.

La sécurité routière est un enjeu majeur de santé publique et de protection des personnes. D'après l'organisation mondiale de la santé (OMS), près de 1,2 millions de personnes meurent chaque année dans le monde suite à des accidents de la route (2015). D’après des données accidentologiques, 36,7% des blessures graves ont pour origine des lésions au thorax (Page et collab., 2012). La biomécanique en sécurité passive a pour rôle d'améliorer notre compréhension du corps humain dans le but de construire de meilleurs outils pour évaluer le risque de blessure.Les modèles numériques d'être humain sont employés pour simuler virtuellement les conditions d'un accident. Aujourd'hui, ils sont de plus en plus utilisés par les constructeurs automobiles et équipementiers pour mieux comprendre les mécanismes lésionnels. Cependant, ils n’existent que dans certaines tailles et ne prennent alors pas en compte les variations morphologiques observées dans la population.L'imagerie médicale 3D donne accès aux géométries des différentes structures anatomiques composant le corps humain. Les hôpitaux regorgent aujourd'hui de quantités d'images 3D couvrant une très large partie de la population en termes d'âge, de corpulence et de sexe.L’objectif global de cette thèse est de modéliser statistiquement la géométrie 3D de la cage thoracique à partir d'images médicales afin de personnaliser un modèle numérique de corps humain pour simuler par éléments finis des conditions de choc automobile. Le premier objectif est d’élaborer un protocole de segmentation une base de CT-scans de manière à obtenir des données géométriques adaptées à la construction d’un modèle statistique de forme de la cage thoracique.Le deuxième objectif est de construire un modèle statistique de forme de la cage thoracique, en prenant en compte sa structure articulée.Le troisième objectif est d’utiliser le modèle statistique de la cage thoracique pour déformer un modèle numérique d’être humain, de manière à étudier l’influence de certains paramètres sur le risque de blessure. / Road safety is a major issue of public health and personal safety. According to the World Health Organization (WHO), nearly 1.2 million people die each year worldwide due to road accidents (2015). According to accident data, 36.7% of serious injuries are caused by thoracic injuries (Page et al., 2012). The aim of biomechanics in passive safety is to improve our understanding of the human body in order to build better tools for assessing the risk of injury.Numerical human body models are used to virtually simulate the conditions of an accident. Today, they are increasingly used by car manufacturers and equipment manufacturers to better understand injury mechanisms. However, they exist only in few sizes and do not take into account the morphological variations observed in the population.3D medical imaging gives access to the geometries of the different anatomical structures that make up the human body. Today, hospitals are full of 3D images covering a very large part of the population in terms of age, body size and sex.The overall objective of this thesis is to statistically model the 3D geometry of the rib cage from medical images in order to personalize a numerical human body model to simulate car crash conditions.The first objective is to develop a segmentation process based on CT-scans in order to obtain geometric data adapted to the construction of a statistical model of shape of the rib cage.The second objective is to build a statistical model of the shape of the rib cage, taking into account its articulated structure.The third objective is to use the statistical model of the rib cage to deform a numerical human body model, in order to study the influence of certain parameters on the risk of injury.

Medical imaging technologies such as MRI, CT, PET, etc. enable the use of higher resolution 3D digital image data for research and clinical treatment. The new technologies provide improved spatial resolution at the cost of increased data processing time. Manual identification of anatomical landmarks is still a common practice in many neuroimaging and other medical imaging applications but it is labor-intensive, subjective, and suffers from intra-/inter- rater inconsistency. This work explored one way of estimating a landmark constellation automatically, consistently, and efficiently. The proposed method demonstrated a successful application on how to effectively utilize image processing in tackling clinical challenges. It is shown that the cooperation of spatial localization using linear model prediction with evolutionary principal components and local search estimation using statistical shape models is capable of effectively extracting important landmark detection information from both morphometric relationships of landmarks and consistent intensity distribution of images. It is accurate (compared to 1.6 mm root mean squared errors of manual labeling of brain landmarks), consistent, reliable in predicting many salient midbrain point landmarks such as ac, pc, MPJ, etc. in a longitudinal, multisubject environment, and throughout large datasets with different modalities and image information such as orientation, spacing, and origin. The framework of linear model estimation method using evolutionary principal components and the idea of local search using statistical shape models are generalized to the detection task for arbitrary number of landmarks in other organs, creatures, or even any other physical objects in the world as long as the landmarks present intensity consistency and satisfy regularity in spatial organization.

This thesis enters on the development of a point-based statistical shape model relying on correspondence probabilities in a sound mathematical framework. Further focus lies on the integration of the model into a segmentation method where a novel approach is taken by combining an explicitly represented shape prior with an implicitly represented segmentation contour. In medical image analysis, the notion of shape is recognized as an important feature to distinguish and analyse anatomical structures. The modeling of shape realized by the concept of statistical shape models constitutes a powerful tool to facilitate the solutions to analysis, segmentation and reconstruction problems. A statistical shape model tries to optimally represent a set of segmented shape observations of any given organ via a mean shape and a variability model. A fundamental challenge in doing statistics on shapes lies in the determination of correspondences between the shape observations. The prevailing assumption of one-to-one point correspondences seems arguable due to uncertainties of the shape surface representations as well as the general di fficulty of pinpointing exact correspondences. In this thesis, the following solution to the point correspondence problem is derived: For all point pairs, a correspondence probability is computed which amounts to representing the shape surfaces by Mixtures of Gaussians. This approach allows to formulate the model computation in a generative framework where the shape observations are interpreted as randomly generated by the model. Based on that, the computation of the model is then treated as an optimization problem. An algorithm is proposed to optimize for model parameters and observation parameters through a single maximum a posteriori criterion which leads to a mathematically sound and unified framework. The method is evaluated and validated in a series of experiments on synthetic and real data. To do so, adequate performance measures and metrics are defined based on which the quality of the new model is compared to the qualities of a classical point-based model and of an established surface-based model that both rely on one-to-one correspondences. A segmentation algorithm is developed which employs the a priori shape knowledge inherent in the statistical shape model to constrain the segmentation contour to probable shapes. An implicit segmentation sheme is chosen instead of an explicit one, which is beneficial regarding topological exibility and implementational issues. The mathematically sound probabilistic shape model enables the challenging integration of an explicit shape prior into an implicit segmentation scheme in an elegant formulation. A maximum a posteriori estimation is developed of a level set function whose zero level set best separates the organ from the background under a shape constraint introduced by the model. This leads to an energy functional which is minimized with respect to the level set using an Euler-Lagrangian equation. Since both the model and the implicitly defined contour are well suited to represent multi-object shapes, an extension of the algorithm to multi-object segmentation is developed which is integrated into the same probabilistic framework. The novel method is evaluated on kidney and hip joint segmentation.

Durch ansteigende Alterserwartung werden Behandlungen für Erkrankungen des Bewegungsapparats immer relevanter. Gerade das Knie wird über die Zeit hinweg stark belastet und ist auch durchgehend in Benutzung. Durch Fehlstellungen, Brüche oder auch Arthrose kann es zum kompletten Ver-schleiß dieses Gelenks kommen. Da in einigen Fällen die konservativen Behandlungsmethoden versagen, wird ein Ersatz des Kniegelenks in Erwägung gezogen. Dieser Ersatz wird heutzutage durch Standardprothesen schon gut abgedeckt. Die Bewegungsfähigkeit kann wiederhergestellt und Schmerzen können reduziert werden. Durch das weiterhin ansteigende Alter kommen immer mehr Revisionsendoprothesen zum Einsatz, da die Lebensdauer einer Knieendoprothese nur begrenzt ist. Um diese zu verlängern, kann man eine an den Bewegungsapparat individuell angepasste Prothese implantieren, die der Belastung besser gewachsen ist. Die Erstellung solcher individueller Prothesen ist Aufgabe des Forschungs- und Entwicklungsprojektes EXPERTEB (EXPERTEB 2018). Um solch eine angepasste Prothese herzustellen, benötigt dies jedoch konkrete Maße des Knies. Diese Arbeit soll ein Verfahren präsentieren, mit dem es möglich ist, anhand von CT-Aufnahmen des Kniegelenks alle nötigen geometrischen Maße zu bestimmen. Kern des Verfahrens ist ein angelerntes Statistical Shape Model (SSM), dass nur die nötigen und zu erwartenden Verformungen des Modells zulässt, sodass es sich an das im Datensatz dargestellte Knie anpassen kann. [... aus der Einleitung]

In the field of craniomaxillofacial (CMF) surgery, surgical planning is an important and necessary procedure due to the complex nature of the craniofacial skeleton. Computed tomography (CT) has brought about a revolution in virtual diagnosis, surgical planning and simulation, and evaluation of treatment outcomes. It provides high-quality 3D image and model of skull for Computer-aided surgical planning system (CSPS). During the planning process, one of the essential steps is to reestablish the dental occlusion. In the first project, a new approach is presented to automatically and efficiently reestablish dental occlusion. It includes two steps. The first step is to initially position the models based on dental curves and a point matching technique. The second step is to reposition the models to the final desired occlusion based on iterative surface-based minimum distance mapping with collision constraints. With linearization of rotation matrix, the alignment is modeled by solving quadratic programming. The simulation was completed on 12 sets of digital dental models. Two sets of dental models were partially edentulous, and another two sets have first premolar extractions for orthodontic treatment. Two validation methods were applied to the articulated models. The results show that using the proposed method, the dental models can be successfully articulated with a small degree of deviations from the occlusion achieved with the gold-standard method. Low contrast resolution in CBCT image has become its major limitation in building skull model. Intensive hand-segmentation is required to reconstruct the skull model. Thin bone images are particularly affected by this limitation. In the second project, a novel segmentation approach is presented based on wavelet active shape model (WASM) for a particular interest in the outer surface of the anterior wall of maxilla. 19 CBCT datasets are used to conduct two experiments. This model-based segmentation approach is validated and compared with three different segmentation approaches. The results show that the performance of this model-based segmentation approach is better than those of the other approaches. It can achieve 0.25 +/- 0.2mm of surface error distance from the ground truth of the bone surface. Field of view (FOV) can be reduced in order to reduce unnecessary radiation dose in CBCT. This ROI imaging is common in most of the dentomaxillofacial imaging and orthodontic practices. However, a truncation effect is created due to the truncation of projection images and becomes one of the limitation in CBCT. In the third project, a method for small region of interest (ROI) imaging and reconstruction of the image of ROI in CBCT and two experiments for measurement of dosage are presented. The first experiment shows at least 60% and 70% of radiation dose can be reduced. It also demonstrates that the image quality was still acceptable with little variation of gray by using the traditional truncation correction approach for ROI imaging. The second experiment demonstrates that the images reconstructed by CBCT reconstruction algorithms without truncation correction can be degraded to unacceptable image quality.

The aim of this thesis is to investigate the shape change in hippocampus due to the atrophy in Alzheimer's disease (AD). To this end, specific algorithms and methodologies were developed to segment the hippocampus from structural magnetic resonance (MR) images and model variations in its shape. We use a multi-atlas based segmentation propagation approach for the segmentation of hippocampus which has been shown to obtain accurate parcellation of brain structures. We developed a supervised method to build a population specific atlas database, by propagating the parcellations from a smaller generic atlas database. Well segmented images are inspected and added to the set of atlases, such that the segmentation capability of the atlas set may be enhanced. The population specific atlases are evaluated in terms of the agreement among the propagated labels when segmenting new cases. Compared with using generic atlases, the population specific atlases obtain a higher agreement when dealing with images from the target population. Atlas selection is used to improve segmentation accuracy. In addition to the conventional selection by image similarity ranking, atlas selection based on maximum marginal relevance (MMR) re-ranking and least angle regression (LAR) sequence are developed for atlas selection. By taking the redundancy among atlases into consideration, diversity criteria are shown to be more efficient in atlas selection which is applicable in the situation where the number of atlases to be fused is limited by the computational resources. Given the segmented hippocampal volumes, statistical shape models (SSMs) of hippocampi are built on the samples to model the shape variation among the population. The correspondence across the training samples of hippocampi is established by a groupwise optimization of the parameterized shape surfaces. The spherical parameterization of the hippocampal surfaces are flatten to facilitate the reparameterization and interpolation. The reparameterization is regularized by viscous fluid, which is solved by a fast implementation based on discrete sine transform. In order to use the hippocampal SSM to describe the shape of an unseen hippocampal surface, we developed a shape parameter estimator based on the expectationmaximization iterative closest points (EM-ICP) algorithm. A symmetric data term is included to achieve the inverse consistency of the transformation between the model and the shape, which gives more accurate reconstruction of the shape from the model. The shape prior modeled by the SSM is used in the maximum a posteriori estimation of the shape parameters, which is shown to enforce the smoothness and avoid the effect of over-fitting. In the study of the hippocampus in AD, we use the SSM to model the hippocampal shape change between the healthy control subjects and patients diagnosed with AD. We identify the regions affected by the atrophy in AD by assessing the spatial difference between the control and AD groups at each corresponding landmark. Localized shape analysis is performed on the regions exhibiting significant inter-group difference, which is shown to improve the discrimination ability of the principal component analysis (PCA) based SSM. The principal components describing the localized shape variability among the population are also shown to display stronger correlation with the decline of episodic memory scores linked to the pathology of hippocampus in AD.