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Context
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In medical imaging studies, one often needs to compare images of hundreds of patients to images of hundreds of normal controls to detect abnormalities caused by a pathology. These abnormalities can be seen as deviations from the normal distribution of a particular structure in terms of position, surface/volume, or overall shape.
Image registration is the central operation in group studies and consists in computing a spatial transformation mapping one subject's anatomy onto another. It should be the most accurate possible. Very often, image registration relies on a unique image modality while many of them are now routinely available (anatomical, functional and diffusion MRI to quote a few). The goal of this work will consist in developing a registration framework able to handle several modalities to produce the most accurate spatial transformations, and to further improve the statistical power of group comparisons.
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Work program
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The first step will consist in a proof of concept giving evidence that image registration accuracy is improved by considering multiple structures and imaging modalities instead of one. Second, a review of the most interesting modalities (anatomical, functional and diffusion MRI) and structures (cortical surface, functional regions, neural fibers) will be made. The registration framework will be adapted to include structures of different kind (lines, surfaces, volumes). Finally, given databases of modalities and structures extracted in Alzheimer patients, numerical atlases of those will be computed and compared to a population of normal controls to infer automatically whether the pathology has a significant impact on one of them.
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