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"Compensation for brain shift in deep brain stimulation electrode placement surgery"

Abstract

Deep brain stimulation (DBS) is a neuromodulation method that treats patients with neurodegenerative disease such as Parkinson’s disease (PD). Accurate placement of electrode leads is essential to achieve optimal clinical outcomes. Although stereotactic frames co-register the image-based pre-operative surgical planning and the surgical field in the OR, brain shift that occurs during surgery degrades the accuracy of preop planning and challenges targeting accuracy. Current clinical practice of guidance involves either microelectrode recording (MER), which provides physiological signals of neurons to assist the localization of electrode leads, or anatomic-based intraoperative imaging techniques such as interventional magnetic resonance imaging (iMRI). However, MER increases the likelihood of multiple lead penetrations which is associated with brain hemorrhage, whereas iMRI is not broadly employed by hospitals due to its high cost. As an alternative, model-based approach provides the potential to estimate brain shift by incorporating limited introspective imaging data in a cheap but effective and efficient manner.

To examine the underlying hypothesis that the error of brain shift in DBS lead placement surgery can be reduced by non-linearly mapping preoperative imaging data to intraoperative imaging equivalence, a biomechanical brain model with the assimilation of sparse data is presented. Specifically, the finite-element-based method models brain as a biphasic poro-elastic medium and computes a field of nodal displacement that can be used to deform preop data. As a key component of this approach, an incomplete displacement field (sparse data) is incorporated into the model computation. This thesis focuses on the work of (1) driving the brain model with surface sparse data that can be both geometry- and feature-based using retrospective computed tomography (CT) data sets; (2) incorporating deep brain sparse data alone into the model and demonstrating its improvement of simulation accuracy over its surface counterparts; (3) Establishing optimal boundary conditions for the brain model with the assimilation of sole deep brain sparse data; (4) Demonstrating the potential of estimating surface displacements using deep brain sparse data via artificial neural networks (ANNs).

Thesis Committee

• Keith Paulsen, PhD (Chair)
• Xiaoyao Fan, PhD
• Joshua Aronson, MD
• James Duncan, PhD
• Alexander Hartov, PhD

Contact

For more information, contact Daryl Laware at daryl.a.laware@dartmouth.edu.