Search

Dual Energy X-ray Absorptiometry (DXA) is a digital x-ray technique designed to quantify bone mineral density (BMD), primarily of the lumbar spine and hip, and hence to provide a measure of bone strength and propensity to fracture. The original technology was based on a pencil beam x-ray source but later systems use a fan beam. Additional functionality includes measurement of hand BMD and vertebral fracture assessment (VFA). Smaller devices have also been developed to measure BMD at peripheral sites in the body. As the numerical value of BMD resulting from this technique is used directly in diagnosis and decisions over patient clinical management, it is critical that the measurements are accurate and precise. The work presented in this thesis includes evaluation of the original pencil beam systems, later fan-beam technology, peripheral devices and additional functionality of DXA. This added to the knowledge of the technical capabilities and limitations of the technology providing an indication of precision, factors that may affect accuracy, radiation dose and machine comparison. Novel quality assurance phantoms were designed and constructed for assessing accuracy, precision and long-term stability of DXA-based hand and lateral morphometry techniques. Research was also conducted into factors associated with BMD, treatment compliance and effectiveness and the role of lateral vertebral assessment. This contributes to the growing body of knowledge on clinical risk factors, medical management and the appropriate use of DXA and VFA The thesis also includes the author's contribution to the national training scheme and development of guidelines which have helped raise standards of the technique nationally. These three areas of work contribute to ensuring the safe use of the new and developing technology and appropriate application within the clinical environment supported by highly trained operators and guidelines.

Neurodegenerative diseases affect large portions of the population worldwide and account for a significant portion of medical spending in associated costs. This dissertation will attempt to utilize graph theory and Magnetic Resonance Imaging (MRI) techniques to monitor disease progression and treatment efficacy. The two forms of neurodegeneration presented here are Alzheimer's Disease (AD) and ischemic stroke. The two MRI methods utilized for this work are diffusion tensor imaging (DTI) and resting-state functional MRI (rs-fMRI). DTI provides a map of diffusional anisotropy based on the Brownian motion of water within the restricted boundaries of tissues. In the absence of direct stimulation, rs-fMRI displays cognitive activity based on the change in signal between oxygenated and deoxygenated hemoglobin to establish correlations between different neuroanatomical regions. In this work, a high angular resolution DTI acquired with 18 diffusion directions was acquired using an 11.75-T, 500-MHz MRI scanner located at the FAMU-FSU College of Engineering and a 21.1-T, 900-MHz MRI scanner located at the National High Magnetic Field Laboratory to perform tractography in 5xFAD and 3xTgAD mouse models of familial Alzheimer's Disease. The 21.1-T scanner was used to perform the same DTI acquisition and rs-fMRI acquisitions on ischemic stroke models in rats, some of which received a human mesenchymal stem cell treatment to evaluate the structural and functional changes that occur over time, post-ischemia. The main neural areas of focus for the DTI studies were the piriform area of the cortex, temporal cortex, parietal cortex and hippocampus. In the rs-fMRI study, regions included the amygdala, hippocampus, midbrain, thalamus and striatum. Changes in the network metrics of weighted degree, eccentricity, clustering coefficient, betweenness centrality, closeness centrality, harmonic closeness centrality, and eigenvector centrality were measured as a function of age/time post-ischemia, phenotype (AD) and treatment regimen.

As the push for autonomous systems gains momentum, there has been a shift from automated to semi-autonomous systems, and current and future applications of artificial intelligence will revolve around the development of autonomous systems. At present, research is being performed to enable autonomous systems in trading, transportation, weapons, aircrafts, homes, drones, robots, and diagnostic imaging. Several companies such as Apple, Google, Microsoft and Tesla as well as NASA, the US Navy and the US military are investing in research on autonomous systems. Autonomous systems may involve the use of a large number of hardware and software components. While sensors are responsible for collecting data about the surroundings, machine learning and algorithms are used to generate useful information from available data and determine the action that should be taken. For most automatic and autonomous systems, it is important for the system to have the capability to detect hazardous situations and provide precise details, enabling either a human to take over or allowing the system itself to handle the situation. While an autonomous system must have sufficient knowledge or training to be able to achieve its ultimate goal, it must be capable of dynamically modifying the path to the goal based on the information obtained by fusing data from various sources or sensors. Besides, several autonomous systems, such as autonomous vehicles or autonomous trading, need fast real-time data processing in order to act accurately in real-time. Finally, the goal of all autonomous systems is to minimize human intervention. This dissertation contributes to the development of autonomous systems through applications that target different aspects of complex autonomous systems and belong to three distinct domains. It includes automatic segmentation of brain tumors, which is a step towards autonomous diagnostic imaging as well as an example of detection and exact identification of a hazardous condition. The automatic generation of high-resolution undistorted MRIs from distorted low-resolution MRIs reduces the amount of human intervention and develops a system that generates usable information from available data. On the other hand, drone self-localization, road segmentation, and extraction of road network graphs involve fusion of data from various types of sensors. Road graph extraction and drone self-localization play an import role in routing, and is crucial in the ability of an unmanned vehicle to reach its final goal. Besides, in the field of autonomous trading, we explore the use of time-series data to predict stock prices and make decisions about real-time stock transactions.

Chan-Vese is a level set method that simultaneously evolves a level set surface and fits locally constant intensity models for the interior and exterior regions to minimize a Mumford-Shah integral. However, the length-based contour regularization in the Chan-Vese formulation is quite simple and too weak for many applications. In this dissertation we introduce a generalization of the Chan-Vese method to evolve a curve where the regularization is based on a Fully Convolutional Neural Network. We also show how to learn the curve model as a Recurrent Neural Network (RNN) using training examples. Our RNN differs from the standard ones because it has the Chan-Vese locally constant intensity model, which gives it better interpretability and flexibility.

Diffusion-weighted image(DWI) and tractography have been developed for decades and are key elements in recent, large-scale efforts for mapping the human brain. The two techniques together provide us a unique possibility to access the macroscopic structure and connectivity of the human brain non-invasively and in vivo. The information obtained not only can help visualize brain connectivity and help segment the brain into different functional areas but also provides tools for understanding some major cognitive diseases such as multiple sclerosis, schizophrenia, epilepsy, etc. There are lots of efforts have been put into this area. On the one hand, a vast spectrum of tractography algorithms have been developed in recent years, ranging from deterministic approaches through probabilistic methods to global tractography; On the other hand, various mathematical models, such as diffusion tensor, multi-tensor model, spherical deconvolution, Q-ball modeling, have been developed to better exploit the acquisition dependent signal of Diffusion-weighted image(DWI). Despite considerable progress in this area, current methods still face many challenges, such as sensitive to noise, lots of false positive/negative fibers, incapable of handling complex fiber geometry and expensive computation cost. More importantly, recent researches have shown that, even with high-quality data, the results using current tractography methods may not be improved, suggesting that it is unlikely to obtain an anatomically accurate map of the human brain solely based on the diffusion profile. Motivated by these issues, this dissertation develops a global approach that incorporates anatomical validated geometric shape prior when reconstructing neuron fibers. The fiber tracts between regions of interest are initialized and updated via deformations based on gradients of the posterior energy defined in this paper. This energy has contributions from diffusion data, shape prior information, and roughness penalty. The dissertation first describes and demonstrates the proposed method on the 2D dataset and then extends it to 3D Phantom data and the real brain data. The results show that the proposed method is relatively immune to issues such as noise, complicated fiber structure like fiber crossings and kissing, false positive fibers, and achieve more explainable tractography results.

Lung cancer has the highest mortality rate of all cancers in both men and women in the United States. The algorithmic detection, characterization, and diagnosis of abnormalities found in chest CT scan images can aid radiologists by providing additional medically-relevant information to consider in their assessment of medical images. Such algorithms, if robustly validated in clinical settings, carry the potential to improve the health of the general population. In this thesis, we first give an analysis of publicly available chest CT scan annotation data, in which we determine upper bounds on expected classification accuracy when certain radiological features are used as inputs to statistical learning algorithms for the purpose of inferring the likelihood of a lung nodule as being either malignant or benign. Second, a statistical extension of the level set method for image segmentation is introduced and applied to both synthetically-generated and real three-dimensional image volumes of lung nodules in chest CT scans, obtaining results comparable to the current state-of-the-art on the latter.

Diffusion tensor imaging (DTI) provides a map of diffusional anisotropy based on the Brownian motion of water within the restricted boundaries of tissues. In this work, a high angular resolution DTI acquired with 18 diffusion directions and four unweighted images was acquired using a 11.75-T, 500-MHz MRI scanner located at the FAMU-FSU College of Engineering in order to perform tractography in amyloid precursor protein/presenilin 1 (APP/PS1) mouse models of familial Alzheimer’s Disease. Evaluating phenotype (APP/PS1 versus wild type) and age (1, 2, 4 and 6 months), a structural network analysis was employed to assess DTI datasets acquired at an in-plane resolution was 100 x 100 microns with a matrix size of 256 x 256, repetition time of 2 s, echo time of 30 ms, diffusion gradient separation of 21 ms and diffusion gradient time of 3 ms. With 15 averages, high signal-to-noise ratios were achieved over an approximate acquisition time of 47 h per sample. This study used all female brains fixed with 4% paraformaldehyde. The five main neural areas of focus were the piriform area of the cortex, temporal cortex, parietal cortex, and left and right hippocampus. A significant decrease in FA of the temporal cortex was identified. Changes in the network metrics of weighted degree, eccentricity, clustering, betweenness centrality, and closeness centrality were observed as a function of age and phenotype.

Machine learning algorithms along with magnetic resonance imaging (MRI) provides promising techniques to overcome the drawbacks of the current clinical screening techniques. In this study the resting-state functional magnetic resonance imaging (fMRI) to see the level of activity in a patient's brain and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to explore the level of improvement of neo-adjuvant chemotherapy in patients with locally advanced breast cancer were considered. As the first project, we considered fMRI of patients before and after they underwent a double-blind smoking cessation treatment. For the first time, this study aims at developing new theory-driven biomarkers by implementing and evaluating novel techniques from resting-state scans that can be used in relapse prediction in nicotine-dependent patients and future treatment efficacy. In this regards, two classes of patients have been studied, one took the drug N-acetylcysteine and the other took a placebo. Our goal was to classify the patients as treatment or non-treatment, based on their fMRI scans. The image slices of brain are used as the variable. We have applied different voxel selection schemes and data reduction algorithms on all images. Then, we compared several multivariate classifiers and deep learning algorithms and also investigated how the different data reductions affect classification performance. For the second part, we have employed multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this study. we determined the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death employing different machine learning algorithms.

Alzheimer's disease (AD) is one of the top 10 leading causes of death in the US; it debilitates memory and impairs cognition. The current core clinical criteria for diagnosis of AD are based on functional deficits and cognitive impairments that do not include the advanced imaging techniques or cerebrospinal fluid analysis; the final confirmation of the disease is only possible at the time of autopsy when neurofibrillary tangles and beta-amyloids are present in a brain tissue examination. The distributions of these particular pathogens (neurofibrillary tangles and beta-amyloids) follow a pattern that is useful to identify different stages of AD at the time of the autopsy by looking at the presence of pathogens in the areas of the brain. The pathogens are first seen in entorhinal/perirhinal cortex, and then spread to hippocampus cornu ammonis subfields, followed by association cortex and finally the rest of the brain. This disease progression is standard and described in NIA-RI guidelines. In the last decades, with the introduction of advanced imaging techniques in research settings, many in vivo based research methods have been focusing on the volumetric measurements of the hippocampus and its subfields in MRI images and using them as additional information for early diagnosis of AD. While the hippocampal volume provides excellent diagnostic aid, it doesn't address both the pathogens associated with AD and the progression of the pathogens within the different subregions of the hippocampus. The hippocampus formation is a complex circuit that spans the temporal lobes and found to have distinctive subregions. These subregions are subject to different influence by AD at different stages. Since the disease progression as seen in pathogen distributions follows a pattern, studying the pattern of the regional changes will allow us to predict which stage the disease is at. These pathological shifts in regions of the brain are studied extensively in ex vivo MRI as well as during autopsy but not in in vivo MRI. Considering that the brain areas with neurofibrillary tangles and beta-amyloids show hypointensity (PD-weighted) and hyperintensity voxels (T2-weighted) in the MRI images, we suggest an in vivo study using normalized MRI images taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We analyze the pattern of changes in the hippocampal region using a volume-based method in normalized T1-weighted MRI and an intensity-based analysis in normalized PD-weighted MRI. We use the volume-based method to calculate the changes in the volume of each of the hippocampus subfields. For the intensity-based method, we count the number of hypointensity (intensity value < 125) voxel combinations from the Gray-Level Co-occurrence Matrix (GLCM); a normalized MRI images are used inorder to minimize intensity variation. We then use the data (volume and intensity changes) to construct decision trees which classify the MRI images into three categories: normal control (NC), mild cognitive impairment (MCI) and AD. We have found that the volume-based decision trees detect AD MRI images with an accuracy of 75 % but failed to detect NC and MCI MRI images with the same level of accuracy. Whereas, with the intensity-based decision trees, we were able to classify MRI images into NC, MCI and AD categories each with an equally high level of accuracy (above 86 %). To find out how reliable the intensity-based method is in classifying MRI images, we introduced noises to our images. The addition of noises forced some adjustments in our decision trees. The accuracy of decision tree classification decreased in the presence of noises. However, even in the presence of the additional noises, we noticed that the intensity-based method outperforms volume-based method. The classification of MRI images improves when both measures (intensity-based and volume-based) are used in constructing our decision trees. This study has demonstrated that the inclusion of the intensity measurements of PD-weighted MRI images in AD studies may provide a more accurate way to model the natural progression of AD in vivo and contribute to the early diagnosis of AD.

A new method for the segmentation of 3D breast lesions in dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) images, using parallel programming with general purpose computing on graphics processing units (GPGPUs), is proposed. The method has two main parts: a pre-processing step and a segmentation algorithm. In the pre-processing step, DCE-MRI images are registered using an intensity-based rigid transformation algorithm based on gradient descent. After the registration, voxels that correspond to breast lesions are enhanced using the Naïve Bayes machine learning classifier. This classifier is trained to identify four different classes inside breast images: lesion, normal tissue, chest and background. Training is performed by manually selecting 150 voxels for each of the four classes from images in which breast lesions have been confirmed by an expert in the field. Thirteen attributes obtained from the kinetic curves of the selected voxels are later used to train the classifier. Finally, the classifier is used to increase the intensity values of voxels labeled as lesions and to decrease the intensities of all other voxels. The post-processed images are used for volume segmentation of the breast lesions using a level set method based on the active contours without edges (ACWE) algorithm. The segmentation algorithm is implemented in OpenCL for the GPGPUs to accelerate the original model by parallelizing two main steps of the segmentation process: the computation of the signed distance function (SDF) and the evolution of the segmented curve. The proposed framework uses OpenGL to display the segmented volume in real time, allowing the physician to obtain immediate feedback on the current segmentation progress. The proposed implementation of the SDF is compared with an optimal implementation developed in Matlab and achieves speedups of 25 and 12 for 2D and 3D images, respectively. Moreover, the OpenCL implementation of the segmentation algorithm is compared with an optimal implementation of the narrow-band ACWE algorithm. Peak speedups of 55 and 40 are obtained for 2D and 3D images, respectively. The segmentation algorithm has been developed as open source software, with different versions for 2D and 3D images, and can be used in different areas of medical imaging as well as in areas within computer vision, such like tracking, robotics and navigation.

With the increasing popularity of information technology, especially electronic imaging techniques, large amount of high dimensional data such as 3D shapes become pervasive in science, engineering and even people's daily life, in the recent years. Though the data quantity is huge, the extraction of relevant knowledge on those data is still limited. How to understand data in a meaningful way is generally an open problem. The specific challenges include finding adequate mathematical representations of data and designing proper algorithms to process them. The existing tools for analyzing high-dimensional data, including 3D shape data, are found to be insufficient as they usually suffer from many factors, such as misalignments, noise, and clutter. This thesis attempts to develop a framework for processing, analyzing and understanding high-dimensional data, especially 3D shapes, by proposing a set of statistical tools including theory, algorithms and optimization applied to practical problems. In particular, the following aspects of shape analysis are considered: 1. A framework adopting the SRNF representation, based on parallel transport of deformations across surfaces in the shape space, leads to statistical analysis on shape data. Three main analyses are conducted under this framework: (1) computing geodesics when either two end surfaces or the starting surface and an initial deformation are given; (2) parallel transporting deformation across surfaces; and (3) sampling random surfaces. 2. Computational efficiency plays an important role in performing statistical shape analysis on large datasets of 3D objects. To speed up the previous method, a framework with numerical solution is introduced by approximating the inverse mapping, and it reduces the computational cost by an order of magnitude. 3. The geometrical and morphological information, or their shapes, of 3D objects can be analyzed explicitly using boundaries extracted from original image scans. An alternative idea is to consider variability in shapes directly from their embedding images. A novel framework is proposed to unify three important tasks, registering, comparing and modeling images. 4. Finally, the spatial deformations learned from registering images are modeled using the GRID based decomposition. This specific model provides a way to decompose a large deformation into local and fundamental ones so that shape differences between images are easily interpretable. We conclude this thesis with conclusions drawn in this research and discuss potential future directions of statistical shape analysis in the last chapter, both from methodological and application aspects.