Mapping Trabecular Bone Fabric Tensor by in Vivo Magnetic Resonance Imaging

The mechanical competence of bone depends upon its quantity, structural arrangement, and chemical composition. Assessment of these factors is important for the evaluation of bone integrity, particularly as the skeleton remodels according to external (e.g. mechanical loading) and internal (e.g. hormonal changes) stimuli. Micro magnetic resonance imaging (µMRI) has emerged as a non-invasive and non-ionizing method well-suited for the repeated measurements necessary for monitoring changes in bone integrity. However, in vivo image-based directional dependence of trabecular bone (TB) has not been linked to mechanical competence or fracture risk despite the existence of convincing ex vivo evidence. The objective of this dissertation research was to develop a means of capturing the directional dependence of TB by assessing a fabric tensor on the basis of in vivo µMRI. To accomplish this objective, a novel approach for calculating the TB fabric tensor based on the spatial autocorrelation function was developed and evaluated in the presence of common limitations to in vivo µMRI. Comparisons were made to the standard technique of mean-intercept-length (MIL). Relative to MIL, ACF was identified as computationally faster by over an order of magnitude and more robust within the range of the resolutions and SNRs achievable in vivo. The potential for improved sensitivity afforded by isotropic resolution was also investigated in an improved µMR imaging protocol at 3T. Measures of reproducibility and reliability indicate the potential of images with isotropic resolution to provide enhanced sensitivity to orientation-dependent measures of TB, however overall reproducibility suffered from the sacrifice in SNR. Finally, the image-derived TB fabric tensor was validated through its relationship with TB mechanical competence in specimen and in vivo µMR images. The inclusion of trabecular bone fabric measures significantly improved the bone volume fraction-based prediction of elastic constants calculated by micro-finite element analysis. This research established a method for detecting TB fabric tensor in vivo and identified the directional dependence of TB as an important determinant of TB mechanical competence

Automated 3D trabecular bone structure analysis of the proximal femur—prediction of biomechanical strength by CT and DXA

The standard diagnostic technique for assessing osteoporosis is dual X-ray absorptiometry (DXA) measuring bone mass parameters. In this study, a combination of DXA and trabecular structure parameters (acquired by computed tomography [CT]) most accurately predicted the biomechanical strength of the proximal femur and allowed for a better prediction than DXA alone. An automated 3D segmentation algorithm was applied to determine specific structure parameters of the trabecular bone in CT images of the proximal femur. This was done to evaluate the ability of these parameters for predicting biomechanical femoral bone strength in comparison with bone mineral content (BMC) and bone mineral density (BMD) acquired by DXA as standard diagnostic technique. One hundred eighty-seven proximal femur specimens were harvested from formalin-fixed human cadavers. BMC and BMD were determined by DXA. Structure parameters of the trabecular bone (i.e., morphometry, fuzzy logic, Minkowski functionals, and the scaling index method [SIM]) were computed from CT images. Absolute femoral bone strength was assessed with a biomechanical side-impact test measuring failure load (FL). Adjusted FL parameters for appraisal of relative bone strength were calculated by dividing FL by influencing variables such as body height, weight, or femoral head diameter. The best single parameter predicting FL and adjusted FL parameters was apparent trabecular separation (morphometry) or DXA-derived BMC or BMD with correlations up to r = 0.802. In combination with DXA, structure parameters (most notably the SIM and morphometry) added in linear regression models significant information in predicting FL and all adjusted FL parameters (up to R adj = 0.872) and allowed for a significant better prediction than DXA alone. A combination of bone mass (DXA) and structure parameters of the trabecular bone (linear and nonlinear, global and local) most accurately predicted absolute and relative femoral bone strength

In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging

La osteoporosis es una enfermedad ósea que se manifiesta con una menor densidad ósea y el deterioro de la arquitectura del hueso esponjoso. Ambos factores aumentan la fragilidad ósea y el riesgo de sufrir fracturas óseas, especialmente en mujeres, donde existe una alta prevalencia. El diagnóstico actual de la osteoporosis se basa en la cuantificación de la densidad mineral ósea (DMO) mediante la técnica de absorciometría dual de rayos X (DXA). Sin embargo, la DMO no puede considerarse de manera aislada para la evaluación del riesgo de fractura o los efectos terapéuticos. Existen otros factores, tales como la disposición microestructural de las trabéculas y sus características que es necesario tener en cuenta para determinar la calidad del hueso y evaluar de manera más directa el riesgo de fractura. Los avances técnicos de las modalidades de imagen médica, como la tomografía computarizada multidetector (MDCT), la tomografía computarizada periférica cuantitativa (HR-pQCT) y la resonancia magnética (RM) han permitido la adquisición in vivo con resoluciones espaciales elevadas. La estructura del hueso trabecular puede observarse con un buen detalle empleando estas técnicas. En particular, el uso de los equipos de RM de 3 Teslas (T) ha permitido la adquisición con resoluciones espaciales muy altas. Además, el buen contraste entre hueso y médula que proporcionan las imágenes de RM, así como la utilización de radiaciones no ionizantes sitúan a la RM como una técnica muy adecuada para la caracterización in vivo de hueso trabecular en la enfermedad de la osteoporosis. En la presente tesis se proponen nuevos desarrollos metodológicos para la caracterización morfométrica y mecánica del hueso trabecular en tres dimensiones (3D) y se aplican a adquisiciones de RM de 3T con alta resolución espacial. El análisis morfométrico está compuesto por diferentes algoritmos diseñados para cuantificar la morfología, la complejidad, la topología y los parámetros de anisotropía del tejido trabecular. En cuanto a la caracterización mecánica, se desarrollaron nuevos métodos que permiten la simulación automatizada de la estructura del hueso trabecular en condiciones de compresión y el cálculo del módulo de elasticidad. La metodología desarrollada se ha aplicado a una población de sujetos sanos con el fin de obtener los valores de normalidad del hueso esponjoso. Los algoritmos se han aplicado también a una población de pacientes con osteoporosis con el fin de cuantificar las variaciones de los parámetros en la enfermedad y evaluar las diferencias con los resultados obtenidos en un grupo de sujetos sanos con edad similar.Los desarrollos metodológicos propuestos y las aplicaciones clínicas proporcionan resultados satisfactorios, presentando los parámetros una alta sensibilidad a variaciones de la estructura trabecular principalmente influenciadas por el sexo y el estado de enfermedad. Por otra parte, los métodos presentan elevada reproducibilidad y precisión en la cuantificación de los valores morfométricos y mecánicos. Estos resultados refuerzan el uso de los parámetros presentados como posibles biomarcadores de imagen en la enfermedad de la osteoporosis.Alberich Bayarri, Á. (2010). In vivo morphometric and mechanical characterization of trabecular bone from high resolution magnetic resonance imaging [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8981Palanci

Error introduced by common reorientation algorithms in the assessment of rodent trabecular morphometry using micro‐computed tomography

Quantitative analyses of bone using micro‐computed tomography (μCT) are routinely employed in preclinical research, and virtual image reorientation to a consistent reference frame is a common processing step. The purpose of this study was to quantify error introduced by common reorientation algorithms in μCT‐based characterization of bone. Mouse and rat tibial metaphyses underwent μCT scanning at a range of resolutions (6–30 μm). A trabecular volume‐of‐interest (VOI) was manually selected. Image stacks were analyzed without rotation, following 45° In‐Plane axial rotation, and following 45° Triplanar rotation. Interpolation was performed using Nearest‐Neighbor, Linear, and Cubic interpolations. Densitometric (bone volume fraction, tissue mineral density, bone mineral density) and morphometric variables (trabecular thickness, trabecular spacing, trabecular number, structural model index) were computed for each combination of voxel size, rotation, and interpolation. Significant reorientation error was measured in all parameters, and was exacerbated at higher voxel sizes, with relatively low error at 6 and 12 μm (max. reorientation error in BV/TV was 2.9% at 6 μm, 7.7% at 12 μm and 36.5% at 30 μm). Considering densitometric parameters, Linear and Cubic interpolations introduced significant error while Nearest‐Neighbor interpolation caused minimal error, and In‐Plane rotation caused greater error than Triplanar. Morphometric error was strongly and intricately dependent on the combination of rotation and interpolation employed. Reorientation error can be eliminated by avoiding reorientation altogether or by “de‐rotating” VOIs from reoriented images back to the original reference frame prior to analysis. When these are infeasible, reorientation error can be minimized through sufficiently high resolution scanning, careful selection of interpolation type, and consistent processing of all images. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2762–2770, 2018.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146417/1/jor24039_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146417/2/jor24039.pd

Geometric Shape Features Extraction Using a Steady State Partial Differential Equation System

A unified method for extracting geometric shape features from binary image data using a steady state partial differential equation (PDE) system as a boundary value problem is presented in this paper. The PDE and functions are formulated to extract the thickness, orientation, and skeleton simultaneously. The main advantages of the proposed method is that the orientation is defined without derivatives and thickness computation is not imposed a topological constraint on the target shape. A one-dimensional analytical solution is provided to validate the proposed method. In addition, two-dimensional numerical examples are presented to confirm the usefulness of the proposed method.Comment: 31 pages, 10 figure

Caractérisation géométrique par la logique floue et simulation de la résorption cellulairement assistée de substituts poreux pour tissus osseux par microtomographie à rayons X

The objective of this thesis is to provide an improved characterization of porous scaffolds. A more focused objective is to provide a computational model simulating the cell mediated resorption process of resorbable bone substitutes. The thesis is structured in three scientific manuscripts. The first manuscript used fuzzy-based image treatment methods to analyse images generated by micro-computed tomography. From the literature, it is known that the fuzzy-based method helps to improve the accuracy of the characterization, in particular for scaffolds featuring a relatively small pore size. In addition, a new algorithm was introduced to determine both pore and interconnection sizes. The surface area of bone substitutes was quantified by using marching cube algorithm. Besides, the so-called Lattice Boltzmann method was used to characterize the permeability of the investigated scaffolds. Scaffolds made of [béta]-tricalcium phosphate ([béta]-Ca[subscript 3](PO[subscript 4])[subscript 2]) and presenting a constant porosity and four variable pore sizes were examined. The average pore size (diameter) of the four bone substitute groups (denominated with a letter from group A to D) was measured to be 170.3«1.7, 217.3«5.2, 415.8«18.8 and 972.3«10.9 [micro]m. Despite this significant change in pore size, the pore interconnection size only increased slightly, in the range of 61.7 to 85.2 [micro]m. The average porosity of the four groups was 52.3«1.5 %. The surface density of scaffolds decreased from 11.5 to 3.3 mm[superscript -1], when the pore size increased. The results revealed that the permeability of scaffolds is in the same order of magnitude and increased from 1.1?10[superscript -10] to 4.1?10[superscript -10] m[superscript 2] with increasing the pore size. The second manuscript was devoted to the use of subvoxelization algorithm and high-resolution scanner, in an attempt to further improve the accuracy of the results, in particular, of the small pore scaffolds. As expected, an increase of the image resolution from 15 to 7.5 [micro]m significantly eased the segmentation process and hence improved scaffold characterization. Subvoxelization also improved the results specifically in terms of interconnection sizes. Specifically, much smaller interconnection sizes were yielded after applying the subvoxelization process. For example, the mean interconnection size of small pore size groups, group A and B, dropped from 63 to 20 and 30 [micro]m, respectively. Furthermore, due to more details obtained from subvoxelization and high-resolution scanning, additional effects so called"boundary effects" were observed. The boundary effects can yield misleading results in terms of interconnection sizes. The means to reduce these effects were proposed. The third manuscript focused on the simulation and understanding of cell mediated resorption of bone graft substitutes. A computer model was developed to simulate the resorption process of four bone substitute groups. [mu]CT data and new"image processing" tools such as labelling and skeletonization were combined in an algorithm to perform the steps of resorption simulation algorithm. The proposed algorithm was verified by comparing simulation results with the analytical results of a simple geometry and biological in vivo data of bone substitutes. A correlation coefficient between the simulation results and both analytical and experimental data, was found to be larger than 0.9. Local resorption process revealed faster resorption in external region specifically at earlier resorption time. This finding is in agreement with the in vivo results. Two definitions were introduced to estimate the resorption rate; volume resorption rate and linear resorption rate. The volume resorption rate was proportional to accessible surface and decreased when the pore size increased, while the linear resorption rate was proportional to thickness of material and increased with increasing the pore size. In addition, the simulation results revealed no effect of resorption direction on resorption behaviour of substitutes. However, the resorption rate of small pore size samples was decreased with increasing the minimum interconnection size required for cell ingrowth, to 100 [micro]m. This thesis combined novel"image processing" tools and subvoxelization method to improve the characterization of porous bone substitutes used in the bone repair process. The improved characterization allowed a more accurate simulation process. The simulation data were consistent with previously obtained biological data of the same group and allows understanding the local resorption process. The available tools and results are expected to help with the design of optimal substitute for bone repair."--Résumé abrégé par UMI

Development of algorithms and methods for three-dimensional image analysis and biomedical applications

2010/2011Tomographic imaging is both the science and the tool to explore the internal structure of objects. The mission is to use images to characterize the static and/or dynamic properties of the imaged object in order to further integrate these properties into principles, laws or theories. Among the recent trends in tomographic imaging, three- dimensional (3D) methods are gaining preference and there is the quest for overcoming the bare qualitative observation towards the extraction of quantitative parameters directly from the acquired images. To this aim, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI), as well as the related micro-scale techniques (μ-CT and μ-MRI), are promising tools for all the fields of science in which non-destructive tests are required. In order to support the interpretation of the images produced by these techniques, there is a growing demand of reliable image analysis methods for the specific 3D domain. The aim of this thesis is to present approaches for effective and efficient three-dimensional image analysis with special emphasis on porous media analysis. State-of-the art as well as innovative tools are included in a special software and hardware solution named Pore3D, developed in a collaboration with the Italian 3rd generation synchrotron laboratory Elettra (Basovizza - Trieste, Italy). Algorithms and methods for the characterization of different kinds of porous media are described. The key steps of image segmentation and skeletonization of the segmented pore space are also discussed in depth. Three different clinical and biomedical applications of quantitative analysis of tomographic images are presented. The reported applications have in common the characterization of the micro-architecture of trabecular bone. The trabecular (or cancellous) bone is a 3D mesh- work of bony trabeculae and void spaces containing the bone marrow. It can then be thought of as a porous medium with an interconnected porous space. To be more specific, the first application aims at characterizing a structure (a tissue engineering scaffold) that has to mimic the architecture of trabecular bone. The relevant features of porosity, pore- and throat-size distributions, connectivity and structural anisotropy indexes are automatically extracted from μ-CT images. The second application is based on ex vivo experiments carried out on femurs and lumbar spines of mice affected by microgravity conditions. Wild type and transgenic mice were hosted in the International Space Station (ISS) for 3 months and the observed bone loss due to the near-zero gravity was quantified by means of synchrotron radiation μ-CT image analysis. Finally, the results of an in vivo study on the risk of fracture in osteoporotic subjects is reported. The study is based on texture analysis of high resolution clinical magnetic resonance (MR) images.XXIV Ciclo198

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome