Abstract #0796

Creating a diffusion tractography-based atlas of human thalamic ventral intermediate nucleus aided by deep learning

Qiyuan Tian¹, Chanon Ngamsombat¹, Berkin Bilgic^1,2, Qiuyun Fan¹, Yuxin Hu³, Jennifer A McNab³, Thomas Witzel¹, Kawin Setsompop^1,2, Jonathan R Polimeni^1,2, and Susie Y Huang^1,2

¹Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States, ²Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States, ³Radiological Sciences Laboratory, Department of Radiology, Stanford University, Stanford, CA, United States

Tremor suppression in the hands of patients with essential tremor can be achieved by lesioning the ventral intermediate nucleus (Vim) of the thalamus using transcranial MR-guided focused ultrasound. Recent work has shown that diffusion MR tractography identifies the Vim more precisely and predicts the degree of tremor suppression. Here, we trained a convolutional neural network (CNN) to automatically segment relevant regions-of-interest including the thalamus, red nucleus, dentate nucleus and handknob region for probabilistic tractography to identify the Vim. We applied the CNN to 200 HCP healthy subjects and created a tractography-based atlas of Vim location, which could aid in neurosurgical guidance.

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