A comparison of geometry- and feature-based sparse data extraction for model-based image updating in deep brain stimulation surgery.

Deep brain stimulation (DBS) electrode placement is a burr-hole procedure for the treatment of patients with neuro- degenerative disease such as Parkinson’s disease, essential tremor and dystonia. Accurate placement of electrodes is the key to optimal surgical outcome. However, the accuracy of pre-operative images used for surgical planning are often degraded by intraoperative brain shift. To compensate for intraoperative target deviation, we have developed a biomechanical model, driven by partially sampled displacements between pre- and postCT, to estimate a whole brain displacement field based on which updated CT (uCT) can be generated. The results of the finite element model depend on sparse data, as the model minimizes the difference between model estimates and sparse data. Existing approaches to extract sparse data from brain surface are typically geometry or feature-based. In this paper, we explore a geometry- based iterative closest point (ICP) algorithm and a feature-based image registration algorithm, and drive the model with 1) geometry-based sparse data only, 2) feature-based sparse data only, and 3) combined data from 1) and 2). We assess the model performance in terms of model-data misfit, as well as target registration errors (TREs) at the anterior commissure (AC) and posterior commissure (PC). Results show that the model driven by the geometry-based sparse data reduced the TREs of preCT from 1.65mm to 1.26 mm and 1.88 mm to 1.58 mm at AC and PC, respectively by compensating majorly along the direction of gravity and the longitudinal axis, whereas feature-based sparse data contributed to the compensation along the lateral direction at PC.