Ozkan Bebek

Intelligent Control Algorithms for Robotic-Assisted Beating Heart Surgery

This paper focuses on the development of control algorithms for intelligent robotic tools that assist off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, the surgeon operates on the beating heart using intelligent robotic instruments. Robotic tools actively cancel the relative motion between the surgical instruments and the point of interest on the beating heart, dynamically stabilizing the heart for the operation. This algorithm is called active relative motion canceling (ARMC). Here, a model-based intelligent ARMC algorithm employing biological signals, such as electrocardiogram, to achieve effective motion cancellation is proposed. Finally, experimental results of the algorithm on a 3-degree-of-freedom robotic test-bed system are reported.

Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic-Assisted Beating Heart Surgery

Robotic-assisted beating heart surgery aims to allow surgeons to operate on a beating heart without stabilizers as if the heart is stationary. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface - a process called active relative motion canceling. Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-squares-based prediction algorithms, using an adaptive filter to generate future position estimates, are implemented and studied. The first method assumes a linear system relation between the consecutive samples in the prediction horizon. On the contrary, the second method performs this parametrization independently for each point over the whole the horizon. The effects of predictor parameters and variations in heart rate on tracking performance are studied with constant and varying heart rate data. The predictors are evaluated using a three-degree-of-freedom (DOF) test bed and prerecorded in vivo motion data. Then, the one-step prediction and tracking performances of the presented approaches are compared with an extended Kalman filter predictor. Finally, the essential features of the proposed prediction algorithms are summarized.

Design of a Parallel Robot for Needle-Based Interventions on Small Animals

In this paper, a novel 5-degrees-of-freedom robot for performing needle-based interventions on small animal subjects is presented. The robot can realize dexterous alignment of the needle using two parallel mechanisms, and has a syringe mechanism to insert needles to subjects. Operations on small animals require high accuracy positioning during needle insertion. The kinematic calibration procedure of the robot using an optical tracker as an external sensor is presented to enhance accuracy of the system. After the kinematic calibration, the positioning accuracy of the needle tip is measured as 0.4 mm RMS. The robot design is light weight, and has a motion bandwidth of 4 Hz. The robot can track reference trajectories with a closed-loop controller.

Improved prediction of heart motion using an adaptive filter for robot assisted beating heart surgery

Robot assisted heart surgery allows surgeons to operate on a heart while it is still beating as if it had been stopped. The robot actively cancels heart motion by closely following a point of interest (POI) on the heart surface -- a process called active relative motion canceling (ARMC). Due to the high bandwidth of the POI motion, it is necessary to supply the controller with an estimate of the immediate future of the POI over a prediction horizon. In this paper, a prediction algorithm, using an adaptive filter to generate future position estimates, is implemented and studied. The effects of predictor parameters on tracking performance are studied. Finally, the predictor is evaluated using a 3 degrees of freedom test-bed and prerecorded heart motion data.

Whisker Sensor Design for Three Dimensional Position Measurement in Robotic Assisted Beating Heart Surgery

In the robotic-assisted off-pump coronary artery bypass graft (CABG) surgery, surgeon performs the operation with intelligent robotic instruments controlled through teleoperation that replace conventional surgical tools. The robotic tools actively cancel the relative motion between the surgical instruments and the point of interest on the beating heart. Measuring the motion of the heart during this operation is an important part of this scheme. In this paper, a novel whisker sensor design to measure the heart motion in three dimensions (3D) is presented. The proposed whisker sensor is a flexible contact sensor. Low stiffness of the sensor prevents damage on the tissue it contacts. This paper explains the design concept, and reports the simulation and measurement results of the prototype whisker position sensor

Personal navigation via shoe mounted inertial measurement units

We are developing a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units. The goal of this project is to develop a navigation system that use secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero velocity durations from the ground reaction sensors are used to reset the accumulated integration errors from the accelerometers and gyroscopes in position calculation. We achieved an average position error of 4 meters at the end of half-hour walks.

Needle localization using gabor filtering in 2D ultrasound images

In the percutaneous needle procedures using ultrasound (US) imaging, the needle should be detected precisely to avoid damage to the tissue and to get the samples from the appropriate site. Excessive artifacts and low resolution of the US images make it difficult to detect the needle and its tip. It is possible to enhance the needle image using image processing; and this work proposes a novel needle detection method in 2D US images based on the Gabor filter. This method enhances the needle outline while suppressing the other structures in the image. First, the needle insertion angle is estimated and then the needle trajectory is found with the RANSAC line estimator. The experiments with three different phantoms showed that the algorithm is robust and could work in percutaneous needle procedures using US images.

Prediction of heartbeat motion with a generalized adaptive filter

In order to perform coronary artery bypass graft surgery, a stationary heart is necessary. A human cannot achieve manual tracking of the complex heartbeat motion. Robotics technology can overcome such limitations. In the robotic-assisted beating heart surgery, the robot actively cancels heart motion by closely following a point of interest on the heart surface-a process called active relative motion canceling. As a result, surgeon can operate on the beating heart as if it is stationary. In this paper, a generalized estimation algorithm, that uses an adaptive filter to generate future position estimates is studied. The predictor is parameterized on-line and adaptively to minimize the prediction error in the mean-square sense. The predictor is evaluated using a 3-degree- of-freedom test-bed system and prerecorded heart motion data.

Whisker-Like Position Sensor for Measuring Physiological Motion

This paper presents the design and characterization of a whisker-like 3-D position sensor. The whisker sensor is a flexible, high-precision, high-bandwidth contact sensor designed for measuring biological motion of soft tissue for medical robotics applications. Low stiffness of the sensor prevents damage on the tissue during its contact. Two different designs, one for measuring large displacements, the other for small displacements are described. Simulation and measurement results from prototype of both designs are reported.

Predictive control algorithms using biological signals for active relative motion canceling in robotic assisted heart surgery

Robotics technology promises an enhanced way of performing off-pump coronary artery bypass graft (CABG) surgery. In the robotic-assisted CABG surgery, surgeon performs the operation with intelligent robotic instruments controlled through teleoperation in place of conventional surgical tools. The robotic tools actively cancel the relative motion between the surgical instruments and the point-of-interest on the beating heart, in contrast to traditional off-pump CABG where the heart is passively constrained to dampen the beating motion. As a result, the surgeon operates on the heart as if it were stationary. This algorithm is called active relative motion canceling (ARMC). In this paper, the use of biological signals, such as electrocardiogram (ECG), to achieve better motion canceling in the model-based intelligent ARMC algorithm is proposed. An ECG contains records for the electrical activity of the heart, which forms a series of waves and complexes. Real time identification of these waves and complexes improve the estimation of the future heart motion and improve the performance of the ARMC algorithm. Finally, the experimental results of the algorithm implemented on a 3-DOF robotic test-bed system are reported