The Influence of Abutment Macro-Design on Peri-Implant Tissue Dimensions for Guided Placed and Restored Implants: A 1-Year Randomized Controlled Trial and CBCT Analysis

Introduction: For optimal dental implant esthetics the transition of a circumferential implant platform to a proper cervical anatomy has been emphasized. This transition is facilitated by the macro-design of the transmucosal portion of the abutment-restoration complex at the provisional and final stages of implant prosthetic therapy. There is limited information from human studies assessing the impact of abutment macro-design on peri-implant tissue dimensional changes. Aim: The aim was to evaluate the peri-implant tissue levels over a 1-year period for implants connected to either convex or concave final abutments at the time of implant placement. Methods: Twenty-eight patients with one missing maxillary premolar randomly allocated to receive one single implant with abutments of different emergence shape configuration. Patients of the CX Group had abutments with convex emergence shape and patients of the CV Group had abutments with concave emergence shape. Clinical and radiographic data collected at the time of implant placement (T0), final prosthesis delivery (T1) and 12 months following implant placement (T2). Results: There was 0.42-0.55mm more bone remodeling occurred in the CX group. Soft tissue thickness was 21-37% greater in the CV group. There was a statistically significant moderate correlation between buccal bone thickness and recession T0-T2. No statistically significant difference found in recession between the two groups. Conclusion: A concave abutment configuration was associated with less bone remodeling and had greater horizontal soft tissue thickness. However, no difference was seen in the amount of recession between the two groups. Bone thickness was found to be the most significant factor for gingival recess

Image based approach for early assessment of heart failure.

In diagnosing heart diseases, the estimation of cardiac performance indices requires accurate segmentation of the left ventricle (LV) wall from cine cardiac magnetic resonance (CMR) images. MR imaging is noninvasive and generates clear images; however, it is impractical to manually process the huge number of images generated to calculate the performance indices. In this dissertation, we introduce a novel, fast, robust, bi-directional coupled parametric deformable models that are capable of segmenting the LV wall borders using first- and second-order visual appearance features. These features are embedded in a new stochastic external force that preserves the topology of the LV wall to track the evolution of the parametric deformable models control points. We tested the proposed segmentation approach on 15 data sets in 6 infarction patients using the Dice similarity coefficient (DSC) and the average distance (AD) between the ground truth and automated segmentation contours. Our approach achieves a mean DSC value of 0.926±0.022 and mean AD value of 2.16±0.60 mm compared to two other level set methods that achieve mean DSC values of 0.904±0.033 and 0.885±0.02; and mean AD values of 2.86±1.35 mm and 5.72±4.70 mm, respectively. Also, a novel framework for assessing both 3D functional strain and wall thickening from 4D cine cardiac magnetic resonance imaging (CCMR) is introduced. The introduced approach is primarily based on using geometrical features to track the LV wall during the cardiac cycle. The 4D tracking approach consists of the following two main steps: (i) Initially, the surface points on the LV wall are tracked by solving a 3D Laplace equation between two subsequent LV surfaces; and (ii) Secondly, the locations of the tracked LV surface points are iteratively adjusted through an energy minimization cost function using a generalized Gauss-Markov random field (GGMRF) image model in order to remove inconsistencies and preserve the anatomy of the heart wall during the tracking process. Then the circumferential strains are straight forward calculated from the location of the tracked LV surface points. In addition, myocardial wall thickening is estimated by co-allocation of the corresponding points, or matches between the endocardium and epicardium surfaces of the LV wall using the solution of the 3D laplace equation. Experimental results on in vivo data confirm the accuracy and robustness of our method. Moreover, the comparison results demonstrate that our approach outperforms 2D wall thickening estimation approaches

Non-destructive quantification of tissue scaffolds and augmentation implants using X-ray microtomography

A three dimensional (3D), interconnected, porous structure is essential for bone tissue engineering scaffolds and skeletal augmentation implants. Current methods of characterising these structures, however, are limited to average properties such as percentage porosity. More accurate quantitative properties, such as pore and interconnect size distributions, are required. Once measured, these parameters need to be correlated to tissue regeneration and integration criteria, including solute transport, blood vessel regeneration, bone ingrowth, and mechanical properties. Ideally, these techniques would work in vitro and in vivo, and hence allow evaluation of osteoconduction and osseointegration after implantation. This thesis will focus on developing and applying algorithms for use with X-ray microtomography (micro-CT or μCT) which can non-destructively image internal structure at the micron scale. The technique will be demonstrated on two separate materials: bioactive glass scaffolds and titanium (Ti) augmentation devices. Using the developed techniques, the structural and compositional evolutions of bioactive glass scaffolds in a simulated body fluid (SBF) flow environment were quantified using micro-CT scans taken at different dissolution stages. Results show that 70S30C bioactive scaffolds retain favourable 3D structures during a 28 d dissolution experiment, with a modal equivalent pore diameter of 682 μm staying unchanged, and a modal equivalent interconnect diameter decreasing from 252 μm to 209 μm. The techniques were then applied to porous Ti augmentation scaffolds. These scaffolds, produced by selective laser melting have very different pore networks with graded randomness and unit size. They present new challenges when applying the developed micro-CT quantification techniques. Using a further adapted methodology, the interconnecting pore sizes, strut thickness, and surface roughness were measured. This demonstrated the robustness of the methodologies and their applicability to a range of tissue scaffolds and augmentation devices

Extracting root system architecture from X-ray micro computed tomography images using visual tracking

X-ray micro computed tomography (µCT) is increasingly applied in plant biology as an imaging system that is valuable for the study of root development in soil, since it allows the three-dimensional and non-destructive visualisation of plant root systems. Variations in the X-ray attenuation values of root material and the overlap in measured intensity values between roots and soil caused by water and organic matter represent major challenges to the extraction of root system architecture. We propose a novel technique to recover root system information from X-ray CT data, using a strategy based on a visual tracking framework embedding a modiffed level set method that is evolved using the Jensen-Shannon divergence. The model-guided search arising from the visual tracking approach makes the method less sensitive to the natural ambiguity of X-ray attenuation values in the image data and thus allows a better extraction of the root system. The method is extended by mechanisms that account for plagiatropic response in roots as well as collision between root objects originating from different plants that are grown and interact within the same soil environment. Experimental results on monocot and dicot plants, grown in different soil textural types, show the ability of successfully extracting root system information. Various global root system traits are measured from the extracted data and compared to results obtained with alternative methods

Accurate geometry reconstruction of vascular structures using implicit splines

3-D visualization of blood vessel from standard medical datasets (e.g. CT or MRI) play an important role in many clinical situations, including the diagnosis of vessel stenosis, virtual angioscopy, vascular surgery planning and computer aided vascular surgery. However, unlike other human organs, the vasculature system is a very complex network of vessel, which makes it a very challenging task to perform its 3-D visualization. Conventional techniques of medical volume data visualization are in general not well-suited for the above-mentioned tasks. This problem can be solved by reconstructing vascular geometry. Although various methods have been proposed for reconstructing vascular structures, most of these approaches are model-based, and are usually too ideal to correctly represent the actual variation presented by the cross-sections of a vascular structure. In addition, the underlying shape is usually expressed as polygonal meshes or in parametric forms, which is very inconvenient for implementing ramification of branching. As a result, the reconstructed geometries are not suitable for computer aided diagnosis and computer guided minimally invasive vascular surgery. In this research, we develop a set of techniques associated with the geometry reconstruction of vasculatures, including segmentation, modelling, reconstruction, exploration and rendering of vascular structures. The reconstructed geometry can not only help to greatly enhance the visual quality of 3-D vascular structures, but also provide an actual geometric representation of vasculatures, which can provide various benefits. The key findings of this research are as follows: 1. A localized hybrid level-set method of segmentation has been developed to extract the vascular structures from 3-D medical datasets. 2. A skeleton-based implicit modelling technique has been proposed and applied to the reconstruction of vasculatures, which can achieve an accurate geometric reconstruction of the vascular structures as implicit surfaces in an analytical form. 3. An accelerating technique using modern GPU (Graphics Processing Unit) is devised and applied to rendering the implicitly represented vasculatures. 4. The implicitly modelled vasculature is investigated for the application of virtual angioscopy

Methods for three-dimensional Registration of Multimodal Abdominal Image Data

Multimodal image registration benefits the diagnosis, treatment planning and the performance of image-guided procedures in the liver, since it enables the fusion of complementary information provided by pre- and intrainterventional data about tumor localization and access. Although there exist various registration methods, approaches which are specifically optimized for the registration of multimodal abdominal scans are only scarcely available. The work presented in this thesis aims to tackle this problem by focusing on the development, optimization and evaluation of registration methods specifically for the registration of multimodal liver scans. The contributions to the research field of medical image registration include the development of a registration evaluation methodology that enables the comparison and optimization of linear and non-linear registration algorithms using a point-based accuracy measure. This methodology has been used to benchmark standard registration methods as well as novel approaches that were developed within the frame of this thesis. The results of the methodology showed that the employed similarity measure used during the registration has a major impact on the registration accuracy of the method. Due to this influence, two alternative similarity metrics bearing the potential to be used on multimodal image data are proposed and evaluated. The first metric relies on the use of gradient information in form of Histograms of Oriented Gradients (HOG) whereas the second metric employs a siamese neural network to learn a similarity measure directly on the image data. The evaluation showed, that both metrics could compete with state of the art similarity measures in terms of registration accuracy. The HOG-metric offers the advantage that it does not require ground truth data to learn a similarity estimation, but instead it is applicable to various data sets with the sole requirement of distinct gradients. However, the Siamese metric is characterized by a higher robustness for large rotations than the HOG-metric. To train such a network, registered ground truth data is required which may be critical for multimodal image data. Yet, the results show that it is possible to apply models trained on registered synthetic data on real patient data. The last part of this thesis focuses on methods to learn an entire registration process using neural networks, thereby offering the advantage to replace the traditional, time-consuming iterative registration procedure. Within the frame of this thesis, the so-called VoxelMorph network which was originally proposed for monomodal, non-linear registration learning is extended for affine and multimodal registration learning tasks. This extension includes the consideration of an image mask during metric evaluation as well as loss functions for multimodal data, such as the pretrained Siamese metric and a loss relying on the comparison of deformation fields. Based on the developed registration evaluation methodology, the performance of the original network as well as the extended variants are evaluated for monomodal and multimodal registration tasks using multiple data sets. With the extended network variants, it is possible to learn an entire multimodal registration process for the correction of large image displacements. As for the Siamese metric, the results imply a general transferability of models trained with synthetic data to registration tasks including real patient data. Due to the lack of multimodal ground truth data, this transfer represents an important step towards making Deep Learning based registration procedures clinically usable