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Abstract #1432

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

Zihao Tang1,2, Mariano Cabezas2, Kain Kyle2,3, Arkiev D'Souza2, Stephen Tisch4, Ben Jonker4, Yael Barnett4, Joel Maamary4, Jerome Maller5, Michael Barnett2,3, Weidong Cai1, and Chenyu Wang2,3
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


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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