441 research outputs found

    A total hip replacement toolbox : from CT-scan to patient-specific FE analysis

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

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    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

    Screening and improving the safe provision of mesenchymal stem cells in regenerative medicine: an in vitro study

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    A number of studies have suggested that mesenchymal stem cells (MSCs) may undergo genetic alterations and spontaneous malignant transformation to form tumour cells, or at least can become contaminated with other cell types following extended periods in culture. Possible transformation or contamination of MSCs during cell culture expansion prior to their use in transplantation therapies is a risk, which should be taken into account. There is a continued need for the development of improved tools for monitoring safety and release criteria of cells intended for cell-based therapies. In order to help address these risks, this thesis aimed to develop improved safety measures in MSC-based cell therapies. Initially, this was investigated through the use of microscopic imaging and image analysis platforms to screen, characterise and distinguish between cultures of non-transformed MSCs and MSC-derived tumour cells, i.e. the osteosarcoma cell lines, SAOS2 and MG63, as well as cells derived from a chondrosarcoma. High content screening (HCS) and live-cell imaging and analysis platforms were used to enable these experiments. Phenotypic features that distinguished the normal versus malignant cell types were identified, including immunoreactivity for the proliferation-associated Ki67 antigen and pluripotency marker Oct4, as well as significant differences in nuclear morphology. These findings help inform release criteria for therapeutic MSCs. To further potentially improve the safety of MSC-based therapies, research was also performed to address the possibility that tumour cells in MSC cultures might remain undetected, thereby still providing a risk in MSC transplantations. Novel combinatorial regimes of potential anti-tumour drugs, i.e. bezafibrate, medroxyprogesterone, and valproic acid (termed V-BAP) were tested in vitro for their effects on MSCs versus SAOS2 and MG63 cells. At determined concentrations, these drugs were shown to significantly inhibit the growth of the osteosarcoma cells, but had little effect on MSCs. Thus, this thesis has made inroads into improved safety of MSC-based therapies by (i) demonstrating the application of imaging-based cell screening platforms to help characterise MSC cultures intended for cell transplantation, and (ii) identifying a novel drug regime that selectively targets osteosarcoma cells whilst having little effect on MSCs. The findings on V-BAP also have application in anti-tumour treatments for osteosarcoma

    Segmentation of pelvic structures from preoperative images for surgical planning and guidance

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    Prostate cancer is one of the most frequently diagnosed malignancies globally and the second leading cause of cancer-related mortality in males in the developed world. In recent decades, many techniques have been proposed for prostate cancer diagnosis and treatment. With the development of imaging technologies such as CT and MRI, image-guided procedures have become increasingly important as a means to improve clinical outcomes. Analysis of the preoperative images and construction of 3D models prior to treatment would help doctors to better localize and visualize the structures of interest, plan the procedure, diagnose disease and guide the surgery or therapy. This requires efficient and robust medical image analysis and segmentation technologies to be developed. The thesis mainly focuses on the development of segmentation techniques in pelvic MRI for image-guided robotic-assisted laparoscopic radical prostatectomy and external-beam radiation therapy. A fully automated multi-atlas framework is proposed for bony pelvis segmentation in MRI, using the guidance of MRI AE-SDM. With the guidance of the AE-SDM, a multi-atlas segmentation algorithm is used to delineate the bony pelvis in a new \ac{MRI} where there is no CT available. The proposed technique outperforms state-of-the-art algorithms for MRI bony pelvis segmentation. With the SDM of pelvis and its segmented surface, an accurate 3D pelvimetry system is designed and implemented to measure a comprehensive set of pelvic geometric parameters for the examination of the relationship between these parameters and the difficulty of robotic-assisted laparoscopic radical prostatectomy. This system can be used in both manual and automated manner with a user-friendly interface. A fully automated and robust multi-atlas based segmentation has also been developed to delineate the prostate in diagnostic MR scans, which have large variation in both intensity and shape of prostate. Two image analysis techniques are proposed, including patch-based label fusion with local appearance-specific atlases and multi-atlas propagation via a manifold graph on a database of both labeled and unlabeled images when limited labeled atlases are available. The proposed techniques can achieve more robust and accurate segmentation results than other multi-atlas based methods. The seminal vesicles are also an interesting structure for therapy planning, particularly for external-beam radiation therapy. As existing methods fail for the very onerous task of segmenting the seminal vesicles, a multi-atlas learning framework via random decision forests with graph cuts refinement has further been proposed to solve this difficult problem. Motivated by the performance of this technique, I further extend the multi-atlas learning to segment the prostate fully automatically using multispectral (T1 and T2-weighted) MR images via hybrid \ac{RF} classifiers and a multi-image graph cuts technique. The proposed method compares favorably to the previously proposed multi-atlas based prostate segmentation. The work in this thesis covers different techniques for pelvic image segmentation in MRI. These techniques have been continually developed and refined, and their application to different specific problems shows ever more promising results.Open Acces

    Machine Learning towards General Medical Image Segmentation

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    The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

    Automatic Bone Structure Segmentation of Under-Sampled CT/FLT-PET Volumes for HSCT Patients

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    In this thesis I present a pipeline for the instance segmentation of vertebral bodies from joint CT/FLT-PET image volumes that have been purposefully under-sampled along the axial direction to limit radiation exposure to vulnerable HSCT patients. The under-sampled image data makes the segmentation of individual vertebral bodies a challenging task, as the boundaries between the vertebrae in the thoracic and cervical spine regions are not well resolved in the CT modality, escaping detection by both humans and algorithms. I train a multi-view, multi-class U-Net to perform semantic segmentation of the vertebral body, sternum, and pelvis object classes. These bone structures contain marrow cavities that, when viewed in the FLT-PET modality, allow us to investigate hematopoietic cellular proliferation in HSCT patients non-invasively. The proposed convnet model achieves a Dice score of 0.9245 for the vertebral body object class and shows qualitatively similar performance on the pelvis and sternum object classes. The final instance segmentation is realized by combining the initial vertebral body semantic segmentation with the associated FLT-PET image data, where the vertebral boundaries become well-resolved by the 28th day post-transplant. The vertebral boundary detection algorithm is a hand-crafted spatial filter that enforces vertebra span as an anatomical prior, and it performs similar to a human for the detection of all but one vertebral boundary in the entirety of the HSCT patient dataset. In addition to the segmentation model, I propose, design, and test a “drop-in” replacement up-sampling module that allows state-of-the-art super-resolution convnets to be used for purely asymmetric upscaling tasks (tasks where only one image dimension is scaled while the other is held to unity). While the asymmetric SR convnet I develop falls short of the initial goal, where it was to be used to enhance the unresolved vertebral boundaries of the under-sampled CT image data, it does objectively upscale medical image data more accurately than naïve interpolation methods and may be useful as a pre-processing step for other medical imaging tasks involving anisotropic pixels or voxels

    The development of a small animal model for assessing the 3D implications of loading on bone microarchitecture

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    It is well established that bone is capable of adapting to changes in its environment; however, little is known regarding how environmental stimuli, specifically loading, are associated with the internal 3D microarchitecture of cortical bone. The aim of this thesis was to develop a small animal model that can be used to experimentally test hypotheses regarding bone adaptation. High resolution micro-CT was validated and employed as a novel method for the visualization and quantification of rat cortical bone microarchitecture in 3D. The use of this imaging method allowed for the measurement of primary vascular canal orientation in 3D, which had never been achieved before. Using this measure along with an immobilization model for unloading allowed me to test how loading is associated with the orientation of these vascular canals. Normally ambulating rat bones (from 10 female rats) had a canal structure that was 9.9° more longitudinal than their immobilized counterparts. This finding that loading has an effect on primary canal orientation brought to light the need to induce remodeling and therefore, secondary vascular canals, in the rat to increase its novelty as a model for looking at bone adaptation. Remodeling was induced by increasing the calcium demands of female rats, either through a calcium restricted diet (n=2) or pregnancy and lactation coupled with a calcium restricted diet (n=2). Mean cortical thickness for the calcium restricted rats and the pregnant and lactating rats that were on a calcium restricted diet were 622 µm and 419 µm, respectively. The mean BMU count for calcium restricted rats seemed to be higher than that of the pregnant and lactating rats; however, the calcium restricted rats seemed to have a lower BMU density. Once this full-scale study is executed the rat will provide a more representative model for studying human bone adaptation

    Intraoperative Quantification of Bone Perfusion in Lower Extremity Injury Surgery

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    Orthopaedic surgery is one of the most common surgical categories. In particular, lower extremity injuries sustained from trauma can be complex and life-threatening injuries that are addressed through orthopaedic trauma surgery. Timely evaluation and surgical debridement following lower extremity injury is essential, because devitalized bones and tissues will result in high surgical site infection rates. However, the current clinical judgment of what constitutes “devitalized tissue” is subjective and dependent on surgeon experience, so it is necessary to develop imaging techniques for guiding surgical debridement, in order to control infection rates and to improve patient outcome. In this thesis work, computational models of fluorescence-guided debridement in lower extremity injury surgery will be developed, by quantifying bone perfusion intraoperatively using Dynamic contrast-enhanced fluorescence imaging (DCE-FI) system. Perfusion is an important factor of tissue viability, and therefore quantifying perfusion is essential for fluorescence-guided debridement. In Chapters 3-7 of this thesis, we explore the performance of DCE-FI in quantifying perfusion from benchtop to translation: We proposed a modified fluorescent microsphere quantification technique using cryomacrotome in animal model. This technique can measure bone perfusion in periosteal and endosteal separately, and therefore to validate bone perfusion measurements obtained by DCE-FI; We developed pre-clinical rodent contaminated fracture model to correlate DCE-FI with infection risk, and compare with multi-modality scanning; Furthermore in clinical studies, we investigated first-pass kinetic parameters of DCE-FI and arterial input functions for characterization of perfusion changes during lower limb amputation surgery; We conducted the first in-human use of dynamic contrast-enhanced texture analysis for orthopaedic trauma classification, suggesting that spatiotemporal features from DCE-FI can classify bone perfusion intraoperatively with high accuracy and sensitivity; We established clinical machine learning infection risk predictive model on open fracture surgery, where pixel-scaled prediction on infection risk will be accomplished. In conclusion, pharmacokinetic and spatiotemporal patterns of dynamic contrast-enhanced imaging show great potential for quantifying bone perfusion and prognosing bone infection. The thesis work will decrease surgical site infection risk and improve successful rates of lower extremity injury surgery
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