Background: Successful navigation in spine surgeries relies on accurate representation of the spine’s interoperative pose. However, its position can move between preoperative imaging and instrumentation. A measure of this motion is a preoperative-to-intraoperative change in lordosis. Objective: To investigate the effect this change has on navigation accuracy and the degree to which an interoperative stereovision system (iSV) for intraoperative patient registration can account for this motion. Methods: For six live pig specimens, a preoperative CT (pCT) was obtained of the lumbar spine in supine position and an interoperative CT in the prone position. Five to six iSV images were intraoperatively acquired of the exposed levels. A fiducial-based registration was performed on a navigation system with the pCT. Separately, the pCT was deformed to match iSV surface data to generate an updated CT (uCT). Navigational accuracy of both the commercial navigation and iSV systems was determined by tracked fiducials and landmarks. Change in lordosis Cobb angle between supine and prone positions was calculated representing preoperative-to-interoperative change in spine pose. Results: The preoperative-to-interoperative change ranged from 12 to 41°. Registration accuracy varied by 4.8 and 1.5 mm for the commercial system (6.2+-1.9 mm) and iSV (3.0+0.6 mm) respectively. Rank correlation shows strong association between increased registration error and positional change for the commercial system (correlation of 0.94, P=0.02) while minimal association for iSV (0.09, P=0.92). Conclusion: Change in spinal pose effects navigational accuracy of commercial systems. iSV shows promise in accounting for these changes given its accuracy is uncorrelated with pose change.
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.
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