Estimating shift at brain surface in deep brain stimulation using machine learning based methods

The success of deep brain stimulation (DBS) depends upon the accurate surgical placement of electrodes in the OR. However, the accuracy of pre-operative scans is often degraded by intraoperative brain shift. To compensate for brain shift, we developed a biomechanical brain model that updates preoperative images by assimilating intraoperative sparse data from either the brain surface or deep brain structures. In addition to constraining the finite element model, surface sparse data estimates model boundary conditions such as the level of cerebrospinal fluid (CSF). As a potentially cost-effective and safe alternative to intraoperative imaging techniques, a machine learning method was proposed to estimate surface brain atrophy by leveraging a large number of ventricle nodal displacements. Specifically, we constructed an artificial neural network (ANN) that consisted of an input layer with 9 hand-engineered features such as the surface-to-ventricle nodal distance. The multilayer perceptron was trained using 132,000 nodal pairs from eleven patient cases and tested using 48,000 from four cases. Results showed that in a testing case, the ANN estimated an overall surface displacement of 8.79 +/- 0.765 mm to the left and 8.26 +/- .455 mm to the right compared to the ground truth (10.36 +/- 1.33 mm left and 7.40 +/- 1.40 mm right). The average prediction error of all four testing cases was less than 2 mm. With further development and evaluation, the proposed method has the potential of supplementing the biomechanical brain model with surface sparse data and estimating boundary parameters.