Robotic alignment of femoral cutting mask during total knee arthroplasty

Convergence Analysis of an Iterative Targeting Method for Keyhole Robotic Surgery

In surgical procedures, robots can accurately position and orient surgical instruments. Intraoperatively, external sensors can localize the instrument and compute the targeting movement of the robot, based on the transformation between the coordinate frame of the robot and the sensor.

This paper addresses the assessment of the robustness of an iterative targeting algorithm in perturbed conditions. Numerical simulations and experiments (with a robot with seven degrees of freedom and an optical tracking system) were performed for computing the maximum error of the rotational part of the calibration matrix, which allows for convergence, as well as the number of required iterations.

The algorithm converges up to 50 degrees of error within a large working space.

The study confirms the clinical relevance of the method because it can be applied on commercially available robots without modifying the internal controller, thus improving the targeting accuracy and meeting surgical accuracy requirements.

Accurate multi-robot targeting for keyhole neurosurgery based on external sensor monitoring

Robotics has recently been introduced in surgery to improve intervention accuracy, to reduce invasiveness and to allow new surgical procedures. In this framework, the ROBOCAST system is an optically surveyed multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system encompasses three robots, connected as a multiple kinematic chain (serial and parallel), totalling 13 degrees of freedom, and it is used to automatically align the probe onto a desired planned trajectory. The probe is then inserted in the brain, towards the planned target, by means of a haptic interface. This paper presents a new iterative targeting approach to be used in surgical robotic navigation, where the multi-robot chain is used to align the surgical probe to the planned pose, and an external sensor is used to decrease the alignment errors. The iterative targeting was tested in an operating room environment using a skull phantom, and the targets were selected on magnetic resonance images. The proposed targeting procedure allows about 0.3 mm to be obtained as the residual median Euclidean distance between the planned and the desired targets, thus satisfying the surgical accuracy requirements (1 mm), due to the resolution of the diffused medical images. The performances proved to be independent of the robot optical sensor calibration accuracy.

Modular multiple sensors information management for computer-integrated surgery

BACKGROUND

In the past 20 years, technological advancements have modified the concept of modern operating rooms (ORs) with the introduction of computer-integrated surgery (CIS) systems, which promise to enhance the outcomes, safety and standardization of surgical procedures. With CIS, different types of sensor (mainly position-sensing devices, force sensors and intra-operative imaging devices) are widely used. Recently, the need for a combined use of different sensors raised issues related to synchronization and spatial consistency of data from different sources of information.

METHODS

In this study, we propose a centralized, multi-sensor management software architecture for a distributed CIS system, which addresses sensor information consistency in both space and time. The software was developed as a data server module in a client-server architecture, using two open-source software libraries: Image-Guided Surgery Toolkit (IGSTK) and OpenCV. The ROBOCAST project (FP7 ICT 215190), which aims at integrating robotic and navigation devices and technologies in order to improve the outcome of the surgical intervention, was used as the benchmark. An experimental protocol was designed in order to prove the feasibility of a centralized module for data acquisition and to test the application latency when dealing with optical and electromagnetic tracking systems and ultrasound (US) imaging devices.

RESULTS

Our results show that a centralized approach is suitable for minimizing synchronization errors; latency in the client-server communication was estimated to be 2 ms (median value) for tracking systems and 40 ms (median value) for US images.

CONCLUSION

The proposed centralized approach proved to be adequate for neurosurgery requirements. Latency introduced by the proposed architecture does not affect tracking system performance in terms of frame rate and limits US images frame rate at 25 fps, which is acceptable for providing visual feedback to the surgeon in the OR.