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Metallic bone implant devices are commonly used to tackle a wide array of bone failures in human patients. The success of such implants relies on the biomechanical and functional bonding between the living bone tissue and implant, a process defined as osseointegration. However, the mechanism of osseointegration is still under debate in the scientific community. One efficient method to help understand this complex process is to characterize the interface between human bones and implant devices after the osseointegration has been established, while another approach is to visualize mineralization in real-time under simulated body conditions. Both of these approaches to understand mineralization have been explored in this thesis. Firstly, due to the inhomogeneous nature of bone and complex topography of implant surfaces, a suitable sample geometry for three-dimensional (3D) characterization was required to fully understand osseointegration. Electron tomography has been proven as an efficient technique to visualize the nanoscale topography of bone-implant interface in 3D. However, resulting from the thickness and shadowing effects of conventional transmission electron microscope (TEM) lamellae at high tilt angles and the limited tilt-range of TEM holders, “missing wedge” artifacts limit the resolution of final reconstructions. In Chapter 3, the exploration of a novel sample geometry to explore osseointegration is reported. Here, on-axis electron tomography based on a needle-shaped sample was applied to solve the problem of the “missing wedge”. This resulted in a near artifact-free 3D visualization of the structure of human bone and laser-modified titanium implant, showing bone growth into the nanotopographies of the implant surface and contributing to the evolution of the definition of osseointegration towards nano-osseointegration. One of the key issues regarding the mechanism of osseointegration that remains is that of the chemical structure at the implant interface, namely distribution of calcium-based and carbon-based components at the interface and their origins. Thus, the second objective of this thesis aimed to push characterization techniques further to four dimensions (4D), by incorporating chemical information as the fourth dimension after the spatial X,Y,Z coordinates. In Chapter 4, correlative 4D characterization techniques including electron energy-loss spectroscopy (EELS) tomography and atom probe tomography (APT) and other spectroscopy techniques were used to probe the nanoscale chemical structure of the bone-implant interface. This work uncovered a transitional biointerphase at the bone-implant interface, consisting of morphological and chemical differences compared to bone away from the interface. Also, a TiN layer between the surface oxide and bulk metal was identified in the laser-modified commercial dental implant. Both findings have implications for the immediate and long-term osseointegration. Since bone formation at the implant interface is a dynamic process, which includes calcium phosphates (CaP) biomineralization as a basis of these reactions, the third objective of this work focused on exploring real-time mineralization processes. Liquid-phase transmission electron microscopy (LP-TEM) is a promising technique to enable real-time imaging with nanoscale spatial resolution and sufficient temporal resolution. In Chapter 5, by using this technique, we present the first real-time imaging of CaP nucleation and growth, which is a direct evidence to demonstrate that CaP mineralization occurs by particle attachment. Overall, this thesis has applied state-of-the-art advanced microscopy techniques to enhance the knowledge and understanding of osseointegration mechanisms by investigating established biointerfaces and real-time mineralization. The developed correlative 4D tomography workflow is transferable to study other interfacial applications in materials science and biological systems, while the LP-TEM work forms a basis for further mineralization research. / Thesis / Doctor of Philosophy (PhD)

​​Electrical resistance tomography (ERT) and electrical impedance tomography (EIT) are imaging techniques reconstructing the internal conductivity distribution image of an object based on voltage measurements at the periphery of the object with a given applied current. ERT uses a direct current (DC), while EIT uses an alternating current (AC). However, for low frequencies both ERT and EIT have the same governing equation, which is often referred to as a non-linear and ill-posed inverse problem. Both methods have diverse applications in biology, biomedicine, and industry. ​This master’s degree project aims to create a low-cost imaging system for the ERT, which is the main focus, as well as for the EIT. The project includes three main components: 1) Simulations and reconstructions using EIDORS (Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software), 2) Developing an experimental workbench (a measurement system), and 3) developing a machine learning model for the ERT. ​EIDORS was used to simulate and reconstruct ERT and EIT images. It was also used to generate training data for the machine learning model to be developed. ​The measurement system includes a circular water tank with electrodes, power supplies, and measurement units. Tanks with 8 and 16 electrodes were designed using 3D printers. Initially, aluminium electrodes provided inconsistent measurements due to magnetization and electrolysis, later replaced by graphite electrodes, offering better but not yet accurate enough results. ​After implementing reconstruction algorithms in EIDORS, a machine learning model was developed for ERT. It involved: 1) generating a training set, containing over 5000 simulated data points, 2) preprocessing the generated data set which included PCA dimensionality reduction, 3) and lastly a linear regression model developed. The model struggled with small object detection and occasional inconclusive results, likely due to limited training dataset diversity. Additionally, images of two cases were reconstructed using EIT and comparing it to ERT it can be concluded that EIT performs better than ERT. ​

High-resolution micro computed tomography (micro-CT) offers 3D image resolution of 1 um for non-destructive evaluation of various samples. However, the micro-CT performance is limited by several factors. Primarily, scan time is extremely long, and sample dimension is restricted by the x-ray beam and the detector size. The latter is the cause for the well-known interior problem. Recent advancement in image reconstruction, spurred by the advent of compressed sensing (CS) theory in 2006 and interior tomography theory since 2007, offers great reduction in the number of views and an increment in the volume of samples, while maintaining reconstruction accuracy. Yet, for a number of reasons, traditional filtered back-projection based reconstruction methods remain the de facto standard on all manufactured scanners. This work demonstrates that CS based global and interior reconstruction methods can enhance the imaging capability of micro-CT scanners. First, CS based few-view reconstruction methods have been developed for use with data from a real micro-CT scanner. By achieving high quality few-view reconstruction, the new approach is able to reduce micro-CT scan time to up to 1/8th of the time required by the conventional protocol. Next, two new reconstruction techniques have been developed that allow accurate interior reconstruction using just a limited number of global scout views as additional information. The techniques represent a significant progress relative to the previous methods that assume a fully sampled global scan. Of the two methods, the second method uses CS techniques and does not place any restrictions on scanning geometry. Finally, analytic and iterative reconstruction methods have been developed for enlargement of the field of view for the interior scan with a small detector. The idea is that truncated projections are acquired in an offset detector geometry, and the reconstruction procedure is performed through the use of a weighting function / weighted iteration updates, and projection completion. The CS based reconstruction yields the highest image quality in the numerical simulation. Yet, some limitations of the CS based techniques are observed in case of real data with various imperfect properties. In all the studies, physical micro-CT phantoms have been designed and utilized for performance analysis. Also, important guidelines are suggested for future improvements. / Ph. D.

This report delves into two of the major truncated data problems in helical cone-beam tomography: Axial truncation and Lateral truncation. The problem of axial truncation, also classically known as the Long Object problem, was a major challenge in the development of helical scan tomography. Generalization of the Feldkamp method (FDK) for circular scan to the helical scan trajectory was known to give reasonable solutions to the problem. The FDK methods are approximate in nature and hence provide only approximate solution to the long object problem. Recently, many methods which provide exact solution to this problem have been developed the major breakthrough being the Katsevich’s algorithm which is exact, efficient and also requires lesser detector area compared to Feldkamp methods. The first part of the report deals with the implementation strategies for methods capable of handling axial truncation. Here, we specifically look at the exact and efficient Katsevich’s solution to long object problem and the class of approximate solutions provided by the generalized FDK formulae. The later half of the report looks at the lateral truncation problem and suggests new methods to handle such truncation in helical scan CT. Simulation results for reconstruction with laterally truncated projection data, assuming it to be complete, gives severe artifacts which even penetrates into the field of view (FOV). A row-by-row data completion approach using Linear Prediction is introduced for helical scan truncated data. An extension/improvement of this technique known as Windowed Linear Prediction approach is introduced. Efficacy of both these techniques are shown using simulation with standard phantoms. Various image quality measures for the resulting reconstructed images are used to evaluate the performance of the proposed methods against an already existing technique. Motivated by a study of the autocorrelation and partial autocorrelation functions of the projection data the use of a non-stationary linear model, the ARIMA model, is proposed for data completion. The new model is first validated in the 2D truncated data situation. Also a method of incorporating the parallel beam data consistency condition into this new method is considered. Performance evaluation of the new method with consistency condition shows that it can outperform the existing techniques. Simulation experiments show the efficacy of the ARIMA model for data completion in 2D as well as 3D truncated data scenario. The model is shown to work well for the laterally truncated helical cone-beam case.

L'imagerie cone-beam computed tomography (CBCT) est une méthodologie de contrôle non destructif permettant l'obtention d'images volumiques d'un objet. Le système d'acquisition se compose d'un tube à rayons X et d'un détecteur plan numérique. La recherche développée dans ce manuscrit se déroule dans le contexte industriel. L'objet est placé sur une platine de rotation et une séquence d'images 2D est acquise. Un algorithme de reconstruction procure des données volumiques de l'atténuation de l'objet. Ces informations permettent de réaliser une étude métrologique et de valider ou non la conformité de la pièce imagée. La qualité de l'image 3D est dégradée par différents artefacts inhérents à la plateforme d'acquisition. L'objectif de cette thèse est de mettre au point une méthode de correction adaptée à une plateforme de micro-tomographie par rayons X d'objets manufacturés poly-matériaux. Le premier chapitre décrit les bases de la physique et de l'algorithmie propres à la technique d'imagerie CBCT par rayons X ainsi que les différents artefacts nuisant à la qualité de l'image finale. Le travail présenté ici se concentre sur deux types d'artefacts en particulier: les rayonnements secondaires issus de l'objet et du détecteur et le durcissement de faisceau. Le second chapitre présente un état de l'art des méthodes visant à corriger le rayonnement secondaire. Afin de quantifier le rayonnement secondaire, un outil de simulation basé sur des techniques de Monte Carlo hybride est développé. Il permet de caractériser le système d'acquisition installé au laboratoire de façon réaliste. Le troisième chapitre détaille la mise en place et la validation de cet outil. Les calculs Monte Carlo étant particulièrement prohibitifs en terme de temps de calcul, des techniques d'optimisation et d'accélération sont décrites. Le comportement du détecteur est étudié avec attention et il s'avère qu'une représentation 2D suffit pour modéliser le rayonnement secondaire. Le modèle de simulation permet une reproduction fidèle des projections acquises avec le système réel. Enfin, le dernier chapitre présente la méthodologie de correction que nous proposons. Une première reconstruction bruitée de l'objet imagé est segmentée afin d'obtenir un modèle voxélisé en densités et en matériaux. L'environnement de simulation fournit alors les projections associées à ce volume. Le volume est corrigé de façon itérative. Des résultats de correction d'images tomographiques expérimentales sont présentés dans le cas d'un objet mono-matériaux et d'un objet poly-matériaux. Notre routine de correction réduit les artefacts de cupping et améliore la description du volume reconstruit. / Cone-beam computed tomography (CBCT) is a standard nondestructive imaging technique related to the acquisition of three-dimensional data. This methodology interests a wide range of applications. An industrial CBCT system comprises an X-ray source and a flat-panel detector. Radiographic images are acquired during a rotation of the object of interest. A reconstruction algorithm leads to a volumic representation of the object and a post-processing routine assesses its validity. Accurate quantitative reconstruction is needed to perform an efficient diagsnotic. However, it is challenged by the presence of different artefacts coming from the acquisition itself. This thesis aims at analyzing and correcting those artefacts in a context of industrial micro-tomography. After an introduction to the physical and algorithmic background of CBCT, the artefacts are presented. Our study adresses two major artefacts: beam hardening and scatter radiations coming from the object and the detector. The second chapter reports on the state of the art in secondary radiation correction. A simulation model of the CBCT imaging chain is developed in a Monte Carlo environment. This model is designed to be realistic in order to get an accurate insight on the processes contributing to the final image formation. The third chapter focuses on the built and validation of the simulation tool. Monte Carlo methods are exact but prohibitively slow. Consequently, acceleration and optimization techniques are used to speed-up the calculations without loss of accuracy. A layer model of the flat-panel detector gives some insight on its secondary radiation behavior. More specifically, we demonstrate that a 2D description of the detector would be sufficient to compute its contribution. Our projection tool fits well with the real system. Finally, the last chapter describes our iterative correction method. The noisy initial reconstruction is segmented into different materials and densities and fed to the simulation framework. Beam hardening and secondary radiations are corrected via the volume reconstructed from the difference between acquired and simulated projections. This correction method is shown to be effective on both mono-material and poly-material objects.

This study examined the effect of neurotoxic chemical exposures on brain processes using Single Photon Emission Computed Tomography (SPECT). A control group carefully screened for good health and minimal chemical exposures was compared to two groups of patients diagnosed with health problems following exposure to pesticides or to organic solvents.

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The scattered field from an object, when illuminated with ultrasound, is useful in the reconstruction of it's cross section - a problem broadly classified as 'tomography'. In many situations of medical imaging, we will be interested in getting to know the location and the extent of growth of the inhomogeneity. The Maximum Likelihood (ML) estimation of the location and the shape parameters (of scale and orientation angle), has been done along with the corresponding CR bounds, for the case of weakly scattering objects, where the Fourier Diffraction Theorem(FDT) holds. It has been found that the a-priori information of a reference object function helps in drastic reduction of the number of receivers and illuminations required. For a polygonal object, the shape is specified, when the corner locations are known. We have formulated the problem as, estimation of the frequencies of sum of undamped sinusoids. The result is a substantial reduction in the number of illuminations and receivers required. For acoustically soft and rigid polygons, where the FDT does not hold, the necessary theory is developed to show the dependence of the scattered field on the corner location, using an On Surface Radiation Condition(OSRC). The corner locations are estimated along similar lines, to the one adopted for the weakly scattering objects.

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