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.
The success of deep brain stimulations (DBS) heavily relies on the accurate placement of electrodes in the operating room (OR). However, the pre-operative images such as MRI and CT for surgical targeting are degraded by brain shift, a combination of brain movement and deformation. One way to compensate for this intra-operative brain shift is to utilize a nonlinear biomechanical brain model to estimate the whole brain deformation based on which an updated MR can be generated. Due to the variability of deformation in both magnitude and direction among different cases, partially sampled intraoperative data (e.g., O-arm, CT) of tissue motion is critical to guide the model estimation. In this paper, we present a method to extract the sparse data by matching brain surface features from pre- and post-operative CTs, followed by the reconstruction of the full 3d-displacement field based on the original spatial information of these 2d points. Specifically, the size and the location of the sparse data were determined based on the pneumocephalus in the post-operative CT. The 2D CT-encoded texture maps from both pre-and post-operative CTs were then registered using Demons algorithm. The final 3d-displacement field in our one-patient-example shows an average lateral shift of 1.42mm, and a shift of 10.11mm in the direction of gravity. The results presented in this work have shown the potential of assimilating the sparse data from intra-operative images into the pipeline of model-based image guidance for DBS in the future.