Fully Automated Point-Based Robotic Neurosurgical Patient Registration Procedure

Abstract

In this study, we have introduced a framework for an automatic patient registration procedure using freely distributed fiducial markers within a robot application in neurosurgery. The localization procedures in the image space and in the physical space are fully automated. We have developed a novel algorithm for finding the point pair correspondence between freely distributed fiducial markers in the image and in the physical space. The algorithm introduces a similarity matrix to maximize the possibility of successful point pairing and to remove the potential outlier points. The correspondence algorithm has been tested in 900,000 computer simulations and also on the real data from five laboratory phantom CT scans and twelve clinical patient CT scans, which were paired with 1415 readings captured with an optical tracking system. Testing of simulated point scenarios showed that the correspondence algorithm has a higher percentage of success when a larger number of fiducial markers and a lower number of outlier points were present. In the 24055 tests on the clinical data, there has been a 100 % success rate.

Frameless stereotactic brain biopsy: A prospective study on robot‐assisted brain biopsies performed on 32 patients by using the RONNA G4 system

“…34 We are also developing an algorithm that will suggest optimal positions of the fiducial markers on a 3D reconstruction of a CT or MRI scan to the neurosurgeon, in the near future. The problem of unwanted marker symmetries which can occur if all markers are placed at equidistant positions (such as a square or other regular polygon) is mitigated with the algorithm and procedure developed by Šuligoj et al 19 We have developed an algorithm that calculates all the distances from all individual markers from the CT scan after the patient is scanned for the operation. If the distances are not unique the neurosurgeon uses a stylus to pinpoint each marker on the patient in the OR manually such that it is registered in the robot coordinate frame.…”

“…Furthermore, the automatic localisation process, implemented in the RONNAplan software, is completed in 2–4 s, compared to the manual localisation that takes approximately 1–2 min. For solving the rigid point‐based registration problem, a correspondence between the physical space and image space fiducial configurations is calculated 19 . The entry and target points for the biopsy trajectory are planned in the RONNAplan software (Figure 1C).…”

“…For solving the rigid point-based registration problem, a correspondence between the physical space and image space fiducial configurations is calculated. 19 The entry and target points for the biopsy trajectory are planned in the RONNAplan software (Figure 1C). All biopsies are planned using preoperative CT scans, not MRI nor image merge.…”

“…The algorithm suggests to the medical personnel, the optimal position of the robot with respect to the patient. In a study by Šuligoj et al 19 . we have investigated the influence of different localisation strategies on the RONNA G4 accuracy.…”

“…The algorithm suggests to the medical personnel, the optimal position of the robot with respect to the patient. In a study by Šuligoj et al 19 we have investigated the influence of different localisation strategies on the RONNA G4 accuracy. For increasing robot positioning accuracy, we have used the orientation correction strategy which restrains the orientation of the robot wrist while localising the patient, in the same orientation as in the operative procedure.…”