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Abstract #3679

Fast, Automated DTI-based Thalamus Nuclei Segmentation

Charles Iglehart1, Adam Bernstein2, Ted Trouard3, Craig Weinkauf4, and Manoj Saranathan5
1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2College of Medicine, University of Arizona, Tucson, AZ, United States, 3Department of Biomedical Engineering, University of Arizona, Tucson, AZ, United States, 4Department of Surgery, University of Arizona, Tucson, AZ, United States, 5Department of Medical Imaging, University of Arizona, Tucson, AZ, United States

Fast, accurate, and automatic thalamus segmentation is critical in evaluating the roles of individual thalamic nuclei in pathology and treatment. Many segmentation techniques developed to date involve the use of Diffusion Tensor MRI and are inhibited by their reliance upon i) time-consuming processing to produce an initial mask for the entire thalamus, and ii) orientation distribution functions incapable of modeling intricate small-scale fiber tract geometries. We present a technique that addresses these issues by i) greatly accelerating the masking process via template-based registration, and ii) using constrained spherical deconvolution to produce enhanced ODFs that drive a modified k-means clustering algorithm.

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