Biomarqueurs de la morphologie du cortex cérébral par imagerie par résonance magnétique (IRM) anatomique : application à la maladie d'Alzheimer

Les modifications de la morphologie du cortex cérébral induites par la maladie d'Alzheimer à ses stades précoces contribuent à l'intérêt croissant à l'égard des biomarqueurs de la morphologie corticale. Ceux-ci permettraient notamment une meilleure compréhension de l'impact de cette pathologie sur l'anatomie cérébrale et une détection plus précoce de la maladie. L'originalité de notre travail par rapport au reste de la littérature est de s'intéresser à la morphologie des surfaces interne (interface substance blanche / substance grise) et externe (interface substance grise / liquide cérébro-spinal) du cortex cérébral. Dans cette perspective, nous avons développé des méthodes d'estimation de la courbure et de la dimension fractale des surfaces corticales. A partir de ces biomarqueurs morphologiques et de l'épaisseur corticale dont la méthode d'estimation a été précédemment développée dans le laboratoire, nous avons exploré l'impact de la maladie d'Alzheimer sur la morphologie du manteau cortical et nous avons évalué leur apport individuel et celui de leur association au diagnostic précoce de la maladie. Nos résultats montrent une influence significative de la pathologie sur la morphologie des sillons et sur celle des circonvolutions des surfaces corticales interne et externe. En termes d'application diagnostique, nous montrons que prises isolément, l'épaisseur corticale présente une meilleure capacité prédictive que la courbure corticale, nous ne constatons en revanche aucune capacité prédictive de la dimension fractale. Par contre, nous montrons que l'utilisation conjointe de l'épaisseur corticale et de la courbure permet une amélioration significative du diagnostic précoce.Morphological alterations of the cortical mantle in early stage of Alzheimer's disease have led to an increasing interest towards morphological biomarkers of the cerebral cortex. By providing a quantitative measure of the cortical shape, morphological biomarkers could provide better understanding of the impact of the disease on the cortical anatomy and play a role in early diagnosis. Therefore, as a primary goal in this study, we developed cortical surface curvature and fractal dimension estimation methods. We then applied those methods, together with the estimation of cortical thickness, to investigate the impact of Alzheimer's disease on the cortical shape as well as the contribution of cortical thickness and cortical curvature to the early diagnosis of Alzheimer's disease. The originality of this work lies in the estimation of sulcal and gyral curvature of the internal (gray matter/white matter boundary) and external (gray matter/cerebrospinal fluid boundary) cortical surfaces in addition to the fractal dimensions of these boundaries. Our results showed significant impact of Alzheimer's disease on sulcal and gyral shapes of the internal and external cortical surfaces. In addition, cortical thickness was found to have better ability than cortical curvature for the early diagnosis of Alzheimer's disease; no significant ability for the early diagnosis was found using fractal dimension. However, we found significant improvement in early diagnosis by combining cortical thickness and cortical curvature

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

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

Signal Processing Using Non-invasive Physiological Sensors

Non-invasive biomedical sensors for monitoring physiological parameters from the human body for potential future therapies and healthcare solutions. Today, a critical factor in providing a cost-effective healthcare system is improving patients' quality of life and mobility, which can be achieved by developing non-invasive sensor systems, which can then be deployed in point of care, used at home or integrated into wearable devices for long-term data collection. Another factor that plays an integral part in a cost-effective healthcare system is the signal processing of the data recorded with non-invasive biomedical sensors. In this book, we aimed to attract researchers who are interested in the application of signal processing methods to different biomedical signals, such as an electroencephalogram (EEG), electromyogram (EMG), functional near-infrared spectroscopy (fNIRS), electrocardiogram (ECG), galvanic skin response, pulse oximetry, photoplethysmogram (PPG), etc. We encouraged new signal processing methods or the use of existing signal processing methods for its novel application in physiological signals to help healthcare providers make better decisions

Intelligent Biosignal Analysis Methods

This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal