Surgical Navigation Technology Based on Augmented Reality and Integrated 3D Intraoperative Imaging: A Spine Cadaveric Feasibility and Accuracy Study.

Efst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinnA cadaveric laboratory study.The aim of this study was to assess the feasibility and accuracy of thoracic pedicle screw placement using augmented reality surgical navigation (ARSN).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.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.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.ARSN without fluoroscopy was feasible and demonstrated higher accuracy than free-hand technique for thoracic pedicle screw placement.N/A

Machine learning for automated 3-dimensional segmentation of the spine and suggested placement of pedicle screws based on intraoperative cone-beam computer tomography

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 &gt; 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.</jats:sec

Accuracy of flat panel detector CT with integrated navigational software with and without MR fusion for single-pass needle placement

PURPOSE: Fluoroscopic systems in modern interventional suites have the ability to perform flat panel detector CT (FDCT) with navigational guidance. Fusion with MR allows navigational guidance towards FDCT occult targets. We aim to evaluate the accuracy of this system using single-pass needle placement in a deep brain stimulation (DBS) phantom. MATERIALS AND METHODS: MR was performed on a head phantom with DBS lead targets. The head phantom was placed into fixation and FDCT was performed. FDCT and MR data sets were automatically fused using the integrated guidance system (iGuide, Siemens). A DBS target was selected on the MR data set. A 10cm, 19G needle was advanced by hand in a single pass using laser crosshair guidance. Radial error was visually assessed against measurement markers on the target and by a second FDCT. 10 needles were placed using CT-MR fusion and 10 needles were placed without MR fusion, targeting based solely off of the FDCT, with fusion steps repeated for every pass. RESULTS: Mean radial error was 2.75±1.39 mm as defined by visual assessment to the center of the DBS target and 2.80±1.43 mm as defined by FDCT to the center of the selected target point. There were no statistically significant differences in error between MR fusion and non-MR guided series. CONCLUSIONS: Single pass needle placement in a DBS phantom using FDCT guidance is associated with a radial error of approximately 2.5–3.0 mm at a depth of approximately 80 mm. This system could accurately target sub-centimeter intracranial lesions defined on MR