Study Design.A cadaveric laboratory study.Objective.The aim of this study was to assess the feasibility and accuracy of thoracic pedicle screw placement using augmented reality surgical navigation (ARSN).Summary of Background Data.Recent advances in spinal navigation have shown improved accuracy in lumbosacral pedicle screw placement but limited benefits in the thoracic spine. 3D intraoperative imaging and instrument navigation may allow improved accuracy in pedicle screw placement, without the use of x-ray fluoroscopy, and thus opens the route to image-guided minimally invasive therapy in the thoracic spine.Methods.ARSN encompasses a surgical table, a motorized flat detector C-arm with intraoperative 2D/3D capabilities, integrated optical cameras for augmented reality navigation, and noninvasive patient motion tracking. Two neurosurgeons placed 94 pedicle screws in the thoracic spine of four cadavers using ARSN on one side of the spine (47 screws) and free-hand technique on the contralateral side. X-ray fluoroscopy was not used for either technique. Four independent reviewers assessed the postoperative scans, using the Gertzbein grading. Morphometric measurements of the pedicles axial and sagittal widths and angles, as well as the vertebrae axial and sagittal rotations were performed to identify risk factors for breaches.Results.ARSN was feasible and superior to free-hand technique with respect to overall accuracy (85% vs. 64%, P < 0.05), specifically significant increases of perfectly placed screws (51% vs. 30%, P < 0.05) and reductions in breaches beyond 4 mm (2% vs. 25%, P < 0.05). All morphometric dimensions, except for vertebral body axial rotation, were risk factors for larger breaches when performed with the free-hand method.Conclusion.ARSN without fluoroscopy was feasible and demonstrated higher accuracy than free-hand technique for thoracic pedicle screw placement.Level of Evidence: N/A

This new combination further increases interventional radiologic capabilities because it provides real-time procedural evaluation and tracking.

OBJECTIVEThe goal of this study was to develop and validate a system for automatic segmentation of the spine, pedicle identification, and screw path suggestion for use with an intraoperative 3D surgical navigation system.METHODSCone-beam CT (CBCT) images of the spines of 21 cadavers were obtained. An automated model-based approach was used for segmentation. Using machine learning methodology, the algorithm was trained and validated on the image data sets. For measuring accuracy, surface area errors of the automatic segmentation were compared to the manually outlined reference surface on CBCT. To further test both technical and clinical accuracy, the algorithm was applied to a set of 20 clinical cases. The authors evaluated the system’s accuracy in pedicle identification by measuring the distance between the user-defined midpoint of each pedicle and the automatically segmented midpoint. Finally, 2 independent surgeons performed a qualitative evaluation of the segmentation to judge whether it was adequate to guide surgical navigation and whether it would have resulted in a clinically acceptable pedicle screw placement.RESULTSThe clinically relevant pedicle identification and automatic pedicle screw planning accuracy was 86.1%. By excluding patients with severe spinal deformities (i.e., Cobb angle > 75° and severe spinal degeneration) and previous surgeries, a success rate of 95.4% was achieved. The mean time (± SD) for automatic segmentation and screw planning in 5 vertebrae was 11 ± 4 seconds.CONCLUSIONSThe technology investigated has the potential to aid surgeons in navigational planning and improve surgical navigation workflow while maintaining patient safety.

The authors found that VasoCT provides clear visualization of flow-diverter stents. The images obtained both intraarterially and intravenously are very promising. The initial results provide a high confidence and reproducibility rate for further utilization of this new technique.

We present the first clinical results obtained with a novel technique: the three-dimensional [3D] roadmap. The major difference from the standard 2D digital roadmap technique is that the newly developed 3D roadmap is based on a rotational angiography acquisition technique with the two-dimensional [2D] fluoroscopic image as an overlay. Data required for an accurate superimposition of the previously acquired 3D reconstructed image on the interactively made 2D fluoroscopy image, in real time, are stored in the 3D workstation and constitute the calibration dataset. Both datasets are spatially aligned in real time; thus, the 3D image is accurately superimposed on the 2D fluoroscopic image regardless of any change in C-arm position or magnification. The principal advantage of the described roadmap method is that one contrast injection allows the C-arm to be positioned anywhere in the space and allows alterations in the distance between the x-ray tube and the image intensifier as well as changes in image magnification. In the clinical setting, the 3D roadmap facilitated intravascular neuronavigation with concurrent reduction of procedure time and use of contrast medium.