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Abstract #1913

CNN denoising of FGATIR MRI improves direct visualization of subcortical anatomy

Benjamin Ades-Aron1,2, Mohammed Elsayed1, Michael Hoch3, Gregory Lemberskiy1, Yao Wang2, Dmitry S. Novikov1, Els Fieremans1, and Timothy M. Shepherd1
1Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University, school of medicine, New York, NY, United States, 2Electrical and computer engineering, New York University, Tandon school of engineering, Brooklyn, NY, United States, 3Radiology, University of Pennsylviania, Philadelphia, PA, United States


The basal ganglia, thalamus and brainstem are affected by movement disorders and contain key targets for functional neurosurgery. Targeting however is based on indirect coordinates originally derived from pneumoencephalograms! 3D Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) can directly visualize potential targeted structures (e.g. dentatorubrothalamic tract), but is signal-starved in clinically-feasible acquisitions. We developed a convolutional neural network to improve FGATIR quality. Expert rater assessment suggested this CNN improved contrast resolution of individual structures and overall clinical image quality of 1-average data to the level of 4-averages. This could further enable investigations of functional neurosurgery for movement disorders.

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