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Abstract #2714

Automatic segmentation of thalamic nuclei using multiple imaging modalities at ultrahigh field

Gaurav Verma1, John W. Rutland1, Rebecca Emily Feldman1, Bradley Neil Delman2, and Priti Balchandani1

1Translational & Molecular Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 2Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Segmenting gray matter structures within the thalamus is complicated by poor inherent T1/T2 contrast. Most existing approaches focus on clustering diffusion data including fiber orientation and short & long distance diffusion directions. We propose a hybrid approach incorporating diffusion data with a recently-developed high T1 contrast imaging sequence known as FGATIR. The proposed algorithm clusters on spatial position, fiber orientation distribution coefficients and anatomical contrast to provide robust, yet fast and fully-automatic segmentation of the thalamic nuclei showing strong agreement to manual segmentation performed by a neuroradiologist. Reliable thalamic nuclei segmentation could facilitate targeted therapies like deep brain stimulation.

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