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Abstract #2023

Super-resolution and CNN denoising to improve the accuracy of small brainstem structure characterization with in vivo diffusion MRI

Benjamin Ades-Aron1, Hong-Hsi Lee1, Heidi Schambra2, Dmitry S. Novikov1, Els Fieremans1, and Timothy Shepherd1
1Radiology, NYU School of Medicine, New York, NY, United States, 2Neurology, NYU Langone, New York, NY, United States

Diffusion MRI should be sensitive to early pathology or functional re-organization changes for small internal brainstem structures associated with ischemia, multiple sclerosis or neurodegeneration. Application of diffusion MRI to brainstem studies is challenged by limited spatial resolution, image distortion from skull base artifacts and bias introduced if diffusion contrast is also used for structure segmentation. We describe and evaluate a novel combination of Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) denoising with deep learning, multi-modal nonlinear image co-registration and super-resolution techniques to improve the accuracy of small internal brainstem structure segmentation on advanced diffusion MRI data.

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