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 accuracy of image guidance in spinal surgery can be compromised by intervertebral motion between preoperative supine CT images and intraoperative prone positioning. Patient registration and image updating approaches have been developed to register CT images with intraoperative spine and compensate for posture and alignment changes. We have developed a hand-held stereovision (HHS) system to acquire intraoperative profiles of the exposed spine and facilitate image registration and surgical navigation during open spinal surgery. First, we calibrated the stereo parameters using a checkerboard pattern, and the mean reprojection error was 0.33 pixel using 42 image pairs. Second, we attached an active tracker to the HHS device to track its location using a commercial navigation system. We performed spatial calibration to find the transformation between camera space and tracker space, and the error was 0.73 ± 0.39 mm. Finally, we evaluated the accuracy of the HHS using an ex-vivo porcine specimen. We used a tracked stylus to acquire locations of landmarks such as spinous and transverse processes, and calculated the distances between these points and the reconstructed stereovision surface. The resulting accuracy was 0.91 ± 0.58 mm, with an overall computational efficiency of ~ 5s for each image pair. Compared to our previous microscope-based stereovision system, the accuracy and efficiency of HHS are similar while HHS is more practical and functional, and would be more broadly applicable in spine procedures.
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.
Intraoperative image guidance using preoperative MR images (pMR) is widely used in neurosurgery, but the accuracy can be compromised by brain deformation as soon as the dura is open. Biomechanical finite element models (FEM) have been developed to compensate for brain deformation that occurs at different surgical stages. Intraoperative sparse data extracted from the exposed cortical surface and/or from deeper brain is used to drive the FEM model to compute wholebrain deformation field and produce model-updated MR (uMR) that matches the surgical scene. In previous studies, we quantified the accuracy using model-data misfit (i.e., the root-mean-square error between model estimates and sparse data), as well as target registration errors (TRE) of surface features (such as vessel junctions), and showed that the accuracy on the cortical surface was ~1-2 mm. However, the accuracy in deeper brain has not been investigated, as it is challenging to obtain subsurface features during surgery for accuracy assessment. In this study, we used intraoperative stereovision (iSV) to extract sparse data, which was employed to drive the FEM model and produce uMR, and acquired co-registered intraoperative ultrasound images (iUS) at different surgical stages in 2 cases for cross validation. We quantify model-data misfit, and compare model updated MR with iUS for qualitative assessment of accuracy in deeper brain. The results show that the model-data misfit was 2.39 and 0.64 mm, respectively, for the 2 cases reported, and uMR aligned well with both iSV and iUS, indicating a good accuracy both on the surface and in deeper brain.