Abstract #3357

Evaluation of the efficacy of a Deep Learning-based Reconstruction in the Connectomic Deep Brain Stimulation

Ki Sueng Choi¹, Martijn Figee², Robert Marc Lebel³, Maggie Fung⁴, Suchandrima Banerjee⁵, Helen S Maybeg⁶, and Jaemin Shin⁴

¹Radiology / Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ²Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States, ³GE Healthcare, Alberta, AB, Canada, ⁴GE Healthcare, New York, NY, United States, ⁵GE Healthcare, Menlo Park, CA, United States, ⁶Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Synopsis

The connectomic DBS approach, stimulation tractographically defined white mater pathways, has been successfully employed in functional neurosurgery, and it demonstrated the feasibility of clinical utility. However, this approach is limited in the clinical environment due to low SNR and various artifacts of DWI. The recent development of deep learning-based MR reconstruction allows us to improve SNR and reduce artifacts. This study evaluated the DL reconstruction method in the field of connectomic DBS using deterministic and probabilistic tractography. Tractography results from DL reconstruction show higher sensitivity for delineating WM pathways in specific DBS targets.

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