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.

Adrian elmi-terander 1,2 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. The insertion of pedicle screws remains one of the critical steps in spine surgeries involving thoracolumbar posterior fixation. The procedure poses risks for complications, as both neural and vascular structures are in close proximity to the pedicles. A meta-analysis by Gelalis et al. reported that 1-6.5% of pedicle screws placed using free-hand technique had a cortical violation >4 mm 1. Meanwhile, minimally invasive spine (MIS) surgery is increasingly preferred due to reductions in blood loss, length of hospital stay, and surgical site infections 2. Accuracy and complication rates, however, are not different 3,4. When relying only on fluoroscopy in MIS, the pedicle perforation rate has been reported to be in the range of 12.5-13.5% 5-7. Since MIS reduces anatomical feedback to the surgeon, it can be argued that image guidance, and perhaps also robotic guidance, has a natural role in this kind of surgery 8. The promises of increased accuracy, decreased complication rates, and reduced radiation exposure to the surgical team has led to an increasing use of robots in MIS surgery 9. There are currently several navigated robotic systems available on the market employing various strategies for 3D planning using pre-or intraoperative fluoroscopy or CT. The most frequently reported ones in the literature are the Mazor robots (MAZOR Robotics Ltd., Caesarea, Israel) and the ROSA Spine system (Medtech S.A., Montpellier, France). The Mazor robot...

OBJECTIVE The aim of this study was to evaluate the accuracy (deviation from the target or intended path) and efficacy (insertion time) of an augmented reality surgical navigation (ARSN) system for insertion of biopsy needles and external ventricular drains (EVDs), two common neurosurgical procedures that require high precision. METHODS The hybrid operating room–based ARSN system, comprising a robotic C-arm with intraoperative cone-beam CT (CBCT) and integrated video tracking of the patient and instruments using nonobtrusive adhesive optical markers, was used. A 3D-printed skull phantom with a realistic gelatinous brain model containing air-filled ventricles and 2-mm spherical biopsy targets was obtained. After initial CBCT acquisition for target registration and planning, ARSN was used for 30 cranial biopsies and 10 EVD insertions. Needle positions were verified by CBCT. RESULTS The mean accuracy of the biopsy needle insertions (n = 30) was 0.8 mm ± 0.43 mm. The median path length was 39 mm (range 16–104 mm) and did not correlate to accuracy (p = 0.15). The median device insertion time was 149 seconds (range 87–233 seconds). The mean accuracy for the EVD insertions (n = 10) was 2.9 mm ± 0.8 mm at the tip with a 0.7° ± 0.5° angular deviation compared with the planned path, and the median insertion time was 188 seconds (range 135–400 seconds). CONCLUSIONS This study demonstrated that ARSN can be used for navigation of percutaneous cranial biopsies and EVDs with high accuracy and efficacy.

Study Design. Observational study. Objective. The aim of this study was to evaluate the accuracy of a new frameless reference marker system for patient tracking by analyzing the effect of vertebral position within the surgical field. Summary of Background Data. Most modern navigation systems for spine surgery rely on a dynamic reference frame attached to a vertebra for tracking the patient. This solution has the drawback of being bulky and obstructing the surgical field, while requiring that the dynamic reference frame is moved between vertebras to maintain accuracy. Methods. An augmented reality surgical navigation (ARSN) system with intraoperative cone beam computed tomography (CBCT) capability was installed in a hybrid operating room. The ARSN system used input from four video cameras for tracking adhesive skin markers placed around the surgical field. The frameless reference marker system was evaluated first in four human cadavers, and then in 20 patients undergoing navigated spine surgery. In each CBCT, the impact of vertebral position in the surgical field on technical accuracy was analyzed. The technical accuracy of the inserted pedicle devices was determined by measuring the distance between the planned position and the placed pedicle device, at the bone entry point. Results. The overall mean technical accuracy was 1.65 ± 1.24 mm at the bone entry point (n = 366). There was no statistically significant difference in technical accuracy between levels within CBCTs (P ≥ 0.12 for all comparisons). Linear regressions showed that null- to negligible parts of the effect on technical accuracy could be explained by the number of absolute levels away from the index vertebrae (r 2 ≤ 0.007 for all, β ≤ 0.071 for all). Conclusion. The frameless reference marker system based on adhesive skin markers is unobtrusive and affords the ARSN system a high accuracy throughout the navigated surgical field, independent of vertebral position. Level of Evidence: 3

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.