The combination of navigation and robotics in spine surgery has the potential to accurately identify and maintain bone entry position and planned trajectory. The goal of this study was to examine the feasibility, accuracy and efficacy of a new robot-guided system for semi-automated, minimally invasive, pedicle screw placement. A custom robotic arm was integrated into a hybrid operating room (OR) equipped with an augmented reality surgical navigation system (ARSN). The robot was mounted on the OR-table and used to assist in placing Jamshidi needles in 113 pedicles in four cadavers. The ARSN system was used for planning screw paths and directing the robot. The robot arm autonomously aligned with the planned screw trajectory, and the surgeon inserted the Jamshidi needle into the pedicle. Accuracy measurements were performed on verification cone beam computed tomographies with the planned paths superimposed. To provide a clinical grading according to the Gertzbein scale, pedicle screw diameters were simulated on the placed Jamshidi needles. A technical accuracy at bone entry point of 0.48 ± 0.44 mm and 0.68 ± 0.58 mm was achieved in the axial and sagittal views, respectively. The corresponding angular errors were 0.94 ± 0.83° and 0.87 ± 0.82°. The accuracy was statistically superior (p < 0.001) to ARSN without robotic assistance. Simulated pedicle screw grading resulted in a clinical accuracy of 100%. This study demonstrates that the use of a semi-automated surgical robot for pedicle screw placement provides an accuracy well above what is clinically acceptable.

BACKGROUND AND PURPOSE: The purpose of this work was to test the feasibility of using high angular resolution diffusion imaging (HARDI)-based multitensor tractography to depict motor pathways in patients with brain tumors. MATERIALS AND METHODS: Ten patients (6 males and 4 females) with a mean age of 52 years (range, 9–77 years) were scanned using a 1.5T clinical MR unit. Single-shot echo-planar imaging was used for diffusion-weighted imaging (repetition time, 6000 ms; excitation time, 88 ms) with a diffusion-sensitizing gradient in 32 orientations and a b-value of 1000 s/mm². Data postprocessing was performed using both the conventional single- and multitensor methods. The depiction rate of the 5 major components of the motor pathways, that is, the lower extremity, trunk, hand, face, and tongue, was assessed. RESULTS: Motor fibers on both lesional and contralesional sides were successfully depicted by both the single-tensor and multitensor techniques. However, with the single-tensor model, the depiction of motor pathways was typically limited to the fibers of trunk areas. With the multitensor technique, at least 4 of 5 major fiber bundles arising from the primary motor cortex could be identified. CONCLUSION: HARDI-based multitensor tractography using a standard b-value (1000 s/mm²) can depict the fiber tracts from the face and tongue regions of the primary motor cortex.

In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.

Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < $0.5$ mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery.

During complex interventions, patient tracking is needed for optimal motion compensation, in order to guide the physician in a minimally invasive way. Nowadays, optical tracking systems are used for tracking of markers. Despite the unobtrusiveness of this technology, the approach is cumbersome because it requires manual placement of markers, which can alter during the operations due to the presence of liquids. To improve the clinical workflow, a new feature tracking algorithm is designed, involving feature detection and tracking without optical markers. The new markers are created with the multispectral imaging. Maximally stable extremal regions (MSER) and Speeded Up Robust Feature (SURF) methods are applied to design features and track natural landmarks, e.g. moles and veins. Both methods are tested and compared in accuracy with the mean shift tracking method. SURF reaches the highest accuracy of 0.257 pixels for images at 430 nm and 0.562 pixels for images at 970 nm. This study shows that incorporating multispectral imaging in the surgical scenario leads to an attractive benefit minimizing the risk of marker obstruction and displacement.