Robust marker detection and high precision measurement for real-time anatomical registration using Taguchi method.

BACKGROUND: Accurate autonomous marker detection and measurement is essential for high precision anatomical registration. The measurement should be in real-time, accurate, and robust to the varied conditions of the operation theatre. METHODS: The purpose is to design and implement a robust real-time algorithm to measure the coordinates of the point on the marker for robot-based autonomous registration and surgery. The algorithm is built in two parts based on the recursive Taguchi method. The first part deals with the detection of markers. In the second part, the center of the marker is located, and the coordinates are measured by fitting the concentric ellipse. RESULTS: Three case studies are presented where the algorithm is tested for extreme conditions of uneven lighting, distorted color, surface distortions, and significant random orientation of the marker. The robustness of the algorithm in successfully detecting and measuring in real-time is presented. CONCLUSION: The algorithm is successfully implemented for real-time detection and coordinate measurement of the markers.

Validation of High Precision Robot-Assisted Methods for Intracranial Applications: Preliminary Study.

BACKGROUND: This work attempts to simulate a robot-based autonomous targeted neurosurgical procedure such as biopsy on a vegetable specimen. The objective of the work is to validate the robot-based autonomous neuroregistration and neuronavigation for neurosurgery in terms of stereotactic navigation and target accuracy. CASE DESCRIPTION: A vegetable (carrot) fixed in a tray was used as a model. The tray was affixed with multiple markers. The robot autonomously registers the subject precisely and subsequently accesses the target. The navigation trajectory closely follows the path from the entry point to the target point, as specified in the medical image. The replication of procedures reveals that the target accuracies are within 1 mm. The results based on the case studies are presented. Intricate cases in terms of entry hole size, depth, and size of the target are considered for both phantom and vegetable trials. CONCLUSIONS: The results of the case studies show enhanced and consistent performance characteristics in terms of accuracy, precision, and repeatability with the added advantage of the economy of time. The case studies serve as validation for a high precision robot-assisted neuroregistration and neuronavigation task for neurosurgery and pave the way for further animal and human trials.

Robot-Based Autonomous Neuroregistration and Neuronavigation: Implementation and Case Studies.

BACKGROUND: To conduct an autonomous robot-based neurosurgical procedure in the least time with high accuracy. Further, to analyze and validate the method. METHODS: The coordinates of the markers and the region of interest in the medical image space are measured. Subsequently, in the real patient space, a set of algorithms plans the robot motion. The surgical robot attached with a camera autonomously navigates toward the fiducial markers to measure the coordinates and conduct the neuroregistration. The robot also facilitates the precise constrained guiding path to navigate the surgical tool to reach the region of interest (target) selected in the image. RESULTS: The phantom was registered within 1 mm accuracy in all cases for all poses. High-precision navigation to the target in all poses was shown. CONCLUSIONS: The robot is successful in conducting hands-off neuroregistration and neuronavigation. The accuracy is considerably higher, and the time taken is lesser relative to the manual procedure.

Autonomous neuro-registration for robot-based neurosurgery.

PURPOSE: Neuro-registration is of primary importance as it has a bearing on the accuracy of neurosurgery. Although the accuracy of surgical robots is within the acceptable medical standards, the overall surgical accuracy is dictated by the errors in the neuro-registration process. The purpose of this work is to automate the neuro-registration process to improve the overall accuracy of the robot-based neurosurgery. METHOD: A highly accurate 6-degree-of-freedom Parallel Kinematic Mechanism (6D-PKM) robot is used for both neuro-registration and neurosurgery. In neuro-registration, after measurement of points in the medical image space, the end-platform of the 6D-PKM surgical robot carrying the camera will autonomously navigate towards the fiducial markers to measure its coordinates in the real patient space. An accurate relationship between the medical image space and the real patient space is established, and the same robot will navigate the surgical tool to the target. RESULTS: In order to validate the proposed method for autonomous neuro-registration, experiments are performed using four phantoms. The four phantoms are as follows: PVC skull model, two acrylic blocks and a glass jar with coaxial shells. These phantoms are specifically designed to simulate the neurosurgical process. All the phantoms are registered successfully using the above-stated method. After autonomous neuro-registration, the coordinates of the target point are determined. Neurosurgery validation is carried out by attaching a 1-mm-diameter needle to the robot platform, which is autonomously traversed to reach the target point passing through the two 2-mm-diameter coaxial holes. The experiments are repeated, and the results reveal very good repeatability. CONCLUSION: A method for autonomous neuro-registration has been developed. The robot has been successfully registered using the above method. After successful neuro-registration the overall accuracy of the robot-based neurosurgery is considerably improved. The other benefits of the above method are as follows: elimination of line-of-sight problem, no need of extra unit for neuro-registration, less time for registration, intraoperative registration, human error reduction and low cost.