Abstract #1432

Towards a personalized MRgFUS treatment for tremor disorders: A study on the number of ablations using deep learning and structural connectivity

Zihao Tang^1,2, Mariano Cabezas², Kain Kyle^2,3, Arkiev D'Souza², Stephen Tisch⁴, Ben Jonker⁴, Yael Barnett⁴, Joel Maamary⁴, Jerome Maller⁵, Michael Barnett^2,3, Weidong Cai¹, and Chenyu Wang^2,3

¹School of Computer Science, University of Sydney, Sydney, Australia, ²Brain and Mind Centre, University of Sydney, Sydney, Australia, ³Sydney Neuroimaging Analysis Centre, Sydney, Australia, ⁴St Vincent's Hospital Sydney, Sydney, Australia, ⁵GE Healthcare Australia, Melbourne, Australia

Synopsis

Disabling tremor is the most common symptom of tremor-dominated Parkinson’s disease (PD) patients. Recently, MR guided focused ultrasound (MRgFUS) has been applied in the clinical environment to treat tremor. However, patients can have different treatment responses to the ablation. Tremor suppression can be observed on some patients after ablating the ventral intermediate nucleus (Vim), while others require further ablations. To provide a tailored treatment, a deep learning technique is introduced in this work to predict whether a subject undergoing MRgFUS will respond to a single ablation in the VIM or require additional lesioning in other regions.

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