Electrical Impedance to Assess Facial Nerve Proximity During Robotic Cochlear Implantation.

Reported studies pertaining to needle guidance suggest that tissue impedance available from neuromonitoring systems can be used to discriminate nerve tissue proximity. In this pilot study, the existence of a relationship between intraoperative electrical impedance and tissue density, estimated from computer tomography (CT) images, is evaluated in the mastoid bone of in vivo sheep. In five subjects, nine trajectories were drilled using an image-guided surgical robot. Per trajectory, five measurement points near the facial nerve were accessed and electrical impedance was measured (≤1 KHz) using a multipolar electrode probe. Micro-CT was used postoperatively to measure the distances from the drilled trajectories to the facial nerve. Tissue density was determined from coregistered preoperative CT images and, following sensitivity field modeling of the measuring tip, tissue resistivity was calculated. The relationship between impedance and density was determined for 29 trajectories passing or intersecting the facial nerve. A monotonic decrease in impedance magnitude was observed in all trajectories with a drill axis intersecting the facial nerve. Mean tissue densities intersecting with the facial nerve (971-1161 HU) were different (p <0.01) from those along safe trajectories passing the nerve (1194-1449 HU). However, mean resistivity values of trajectories intersecting the facial nerve (14-24 Ωm) were similar to those of safe passing trajectories (17-23 Ωm). The determined relationship between tissue density and electrical impedance during neuromonitoring of the facial nerve suggests that impedance spectroscopy may be used to increase the accuracy of tissue discrimination, and ultimately improve nerve safety distance assessment in the future.

Estimation of tool pose based on force-density correlation during robotic drilling.

The application of image-guided systems with or without support by surgical robots relies on the accuracy of the navigation process, including patient-to-image registration. The surgeon must carry out the procedure based on the information provided by the navigation system, usually without being able to verify its correctness beyond visual inspection. Misleading surrogate parameters such as the fiducial registration error are often used to describe the success of the registration process, while a lack of methods describing the effects of navigation errors, such as those caused by tracking or calibration, may prevent the application of image guidance in certain accuracy-critical interventions. During minimally invasive mastoidectomy for cochlear implantation, a direct tunnel is drilled from the outside of the mastoid to a target on the cochlea based on registration using landmarks solely on the surface of the skull. Using this methodology, it is impossible to detect if the drill is advancing in the correct direction and that injury of the facial nerve will be avoided. To overcome this problem, a tool localization method based on drilling process information is proposed. The algorithm estimates the pose of a robot-guided surgical tool during a drilling task based on the correlation of the observed axial drilling force and the heterogeneous bone density in the mastoid extracted from 3-D image data. We present here one possible implementation of this method tested on ten tunnels drilled into three human cadaver specimens where an average tool localization accuracy of 0.29 mm was observed.

High-accuracy patient-to-image registration for the facilitation of image-guided robotic microsurgery on the head.

Image-guided microsurgery requires accuracies an order of magnitude higher than today's navigation systems provide. A critical step toward the achievement of such low-error requirements is a highly accurate and verified patient-to-image registration. With the aim of reducing target registration error to a level that would facilitate the use of image-guided robotic microsurgery on the rigid anatomy of the head, we have developed a semiautomatic fiducial detection technique. Automatic force-controlled localization of fiducials on the patient is achieved through the implementation of a robotic-controlled tactile search within the head of a standard surgical screw. Precise detection of the corresponding fiducials in the image data is realized using an automated model-based matching algorithm on high-resolution, isometric cone beam CT images. Verification of the registration technique on phantoms demonstrated that through the elimination of user variability, clinically relevant target registration errors of approximately 0.1 mm could be achieved.