Intraoperative mixed-reality spinal neuronavigation system: a novel navigation technique for spinal intradural pathologies

OBJECTIVE

The objective of this study was to assess the intraoperative accuracy and feasibility of 3D-printed marker-based mixed-reality neurosurgical navigation for spinal intradural pathologies.

METHODS

The authors produced 3D segmentations of spinal intradural tumors with neighboring structures by using combined CT and MRI, and preoperative registration of pathology and markers was successfully performed. A patient-specific, surgeon-facilitated application for mobile devices was built, and a mixed-reality light detection and ranging (LIDAR) camera on a mobile device was employed for cost-effective, high-accuracy spinal neuronavigation.

RESULTS

Mobile device LIDAR cameras can successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The surgeon can visualize and manipulate 3D segmentations of the pathology intraoperatively in 3D.

CONCLUSIONS

A 3D-printed marker-based mixed-reality spinal neuronavigation technique was performed in spinal intradural pathology procedures and has potential to be clinically feasible and easy to use for surgeons, as well as being time saving, cost-effective, and highly precise for spinal surgical procedures.

Three-dimensional-printed marker-based augmented reality neuronavigation: a new neuronavigation technique

OBJECTIVE

The aim of this study was to assess the precision and feasibility of 3D-printed marker-based augmented reality (AR) neurosurgical navigation and its use intraoperatively compared with optical tracking neuronavigation systems (OTNSs).

METHODS

Three-dimensional-printed markers for CT and MRI and intraoperative use were applied with mobile devices using an AR light detection and ranging (LIDAR) camera. The 3D segmentations of intracranial tumors were created with CT and MR images, and preoperative registration of the marker and pathology was performed. A patient-specific, surgeon-facilitated mobile application was developed, and a mobile device camera was used for neuronavigation with high accuracy, ease, and cost-effectiveness. After accuracy values were preliminarily assessed, this technique was used intraoperatively in 8 patients.

RESULTS

The mobile device LIDAR camera was found to successfully overlay images of virtual tumor segmentations according to the position of a 3D-printed marker. The targeting error that was measured ranged from 0.5 to 3.5 mm (mean 1.70 ± 1.02 mm, median 1.58 mm). The mean preoperative preparation time was 35.7 ± 5.56 minutes, which is longer than that for routine OTNSs, but the amount of time required for preoperative registration and the placement of the intraoperative marker was very brief compared with other neurosurgical navigation systems (mean 1.02 ± 0.3 minutes).

CONCLUSIONS

The 3D-printed marker-based AR neuronavigation system was a clinically feasible, highly precise, low-cost, and easy-to-use navigation technique. Three-dimensional segmentation of intracranial tumors was targeted on the brain and was clearly visualized from the skin incision to the end of surgery.