Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model

Kristen Chen; Chen Li; Joshua Aronson; Xiaoyao Fan; Keith Paulsen

doi:10.1117/12.2654450

5 May 2023 Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model

Kristen Chen, Chen Li, Joshua Aronson, Xiaoyao Fan, Keith Paulsen

Author Affiliations +

Proceedings Volume 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling; 124662L (2023) https://doi.org/10.1117/12.2654450
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

The success of deep brain stimulation (DBS) is dependent on the accurate placement of electrodes in the operating room (OR). However, due to intraoperative brain shift, the accuracy of pre-operative scans and pre-surgical planning are often degraded. To compensate for brain shift, we created a finite element bio-mechanical brain model that updates preoperative images by assimilating intraoperative sparse data from the brain surface or deep brain targets. Additionally, we constructed an artificial neural network (ANN) that leveraged a large number of ventricle nodal displacements to estimate brain shift. The machine learning method showed potential in incorporating ventricle sparse data to accurately compute shift at the brain surface. Thus, in this paper, we propose using this machine learning model to estimate brain atrophy at deep brain targets such as the anterior commissure (AC) and the posterior commissure (PC). The ANN consists of an input layer with nine hand-engineered features, such as the distance between the deep brain target and the ventricle node, two hidden layers and an output layer. This model was trained using eight patient cases and tested on two patient cases.

Citation Download Citation

Kristen Chen, Chen Li, Joshua Aronson, Xiaoyao Fan, and Keith Paulsen "Estimating shift at deep brain targets in deep brain stimulation: a comparison between a machine learning approach and a biomechanical model", Proc. SPIE 12466, Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling, 124662L (5 May 2023); https://doi.org/10.1117/12.2654450

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