Medical imaging is a cornerstone of basic science and clinical practice. To discover new mechanisms and markers of disease and their crucial implications for clinical practice, large multi-center imaging studies are acquiring terabytes of complex multi-modality imaging data cross-sectionally and longitudinally over decades. The statistical analysis of data from such studies is challenging due to the complex structure of the imaging data acquired and the ultra-high dimensionality. Furthermore, the heterogeneity of anatomy, pathology, and imaging protocols causes instability and failure of many current state-of-the-art image analysis methods. This grant proposes statistical frameworks for studying populations through biomedical imaging, scalable and robust methods for the identification and accurate quantification of pathology, and analytic tools for the cross-sectional and longitudinal examination of etiology and disease progression. These techniques will be applied to address key goals of the motivating large and multi- center studies of multiple sclerosis and Alzheimer's disease conducted at Johns Hopkins Hospital, the National Institute of Neurological Disorders and Stroke, and across the globe. The project will create methods for uncovering and quantifying brain lesion pathology, incidence, and trajectory. Methods developed under this grant will be targeted towards these neuroimaging goals, but will form the basis for statistical image analysis methods applicable broadly in the biomedical sciences.
This project involves the development of statistical frameworks and methods for the analysis of complex ultra-high-dimensional biomedical imaging. Methods developed are applied to study the clinical management and etiology of multiple sclerosis and Alzheimer's disease longitudinally and cross-sectionally.
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