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
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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