In Search Of Robustness And Efficiency Via L1− And L2− Regularized Optimization For Physiological Motion Compensation (original) (raw)
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The International Journal of Robotics Research, 2009
3D ultrasound imaging has enabled minimally invasive, beating heart intracardiac procedures. However, rapid heart motion poses a serious challenge to the surgeon that is compounded by significant time delays and noise in 3D ultrasound. This paper investigates the concept of using a one-degree-of-freedom motion compensation system to synchronize with tissue motions that may be approximated by 1D motion models. We characterize the motion of the mitral valve annulus and show that it is well approximated by a 1D model. The subsequent development of a motion compensation instrument (MCI) is described, as well as an extended Kalman filter (EKF) that compensates for system delays. The benefits and robustness of motion compensation are tested in user trials under a series of non-ideal tracking conditions. Results indicate that the MCI provides an approximately 50% increase in dexterity and 50% decrease in force when compared with a solid tool, but is sensitive to time delays. We demonstrate that the use of the EKF for delay compensation restores performance, even in situations of high heart rate variability. The resulting system is tested in an in vitro 3D ultrasound-guided servoing task, yielding accurate tracking (1.15 mm root mean square) in the presence of noisy, timedelayed 3D ultrasound measurements.
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Off-pump totally endoscopic coronary artery bypass grafting is a milestone for cardiac surgery, and still a technical challenge. Indeed, the fast and complex cardiac motion makes this operating method technically demanding. Therefore, several robotic systems have been designed to assist the surgeons by compensating for the cardiac motion and providing a virtually motionless operating area. In the proposed systems, the servoing schemes often take advantage of a prediction algorithm that supplies the controller with some future heart motion. This prediction enlarges the control-loop bandwidth, thus allowing a better motion compensation. Obviously, improving the prediction accuracy will lead to better motion-compensation results. Thus, a current challenge in computer-assisted cardiac surgery research is the design of efficient heart-motion-prediction algorithms. In this paper, a detailed survey of the main existing prediction approaches is given and a classification is provided. Then, a novel prediction technique based on amplitude modulation is proposed, and compared with other techniques using in vivo collected datasets. A final discussion summarizes the main features of all the proposed approaches.
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Minimally invasive beating-heart surgery offers substantial benefits for the patient, compared to conventional open surgery. Nevertheless, the motion of the heart poses increased requirements to the surgeon. To support the surgeon, algorithms for an advanced robotic surgery system are proposed, which offer motion compensation of the beating heart. This implies the measurement of heart motion, which can be achieved by tracking natural landmarks. In most cases, the investigated affine tracking scheme can be reduced to an efficient block matching algorithm allowing for realtime tracking of multiple landmarks. Fourier analysis of the motion parameters shows two dominant peaks, which correspond to the heart and respiration rates of the patient.
Heart Motion Compensation for Robotic-Assisted Surgery\\ Predictive Approach vs. Active Observer
In beating-heart surgeries, two natural disturbances affect surgeon's skills: respiration and heartbeats. Heart motions are too dynamic to be manually compensated, therefore robotic-assisted surgery has the potential to improve surgical tasks, especially those requiring high precision such as needle insertion or suturing. In our approach only force data are used to autonomously compensate physiological motions. Two force control architectures are confronted and experimentally compared, using a lightweight 7 DOF WAM arm. One is based on linear predictive control, which uses a mathematical model to predict the system behavior. The other one is based on modelreference adaptive control with stochastic active observers.
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2008
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Cardiac Motion Estimation from 3D Echocardiography with Spatiotemporal Regularization
Cardiac deformation and motion analysis is important for studying heart function and mechanics. Deformation and motion abnormality of the myocardial wall is usually associated with ischemia and infarct. Three-dimensional (3D) echocardiographic (echo) imaging is the most widely used method to estimate cardiac motion. However, quantitative motion analysis from echo images is still a challenging problem due to the complexity of cardiac motion, limitations in spatial and temporal resolutions, low signal noise ratio and imaging artifacts such as signal dropout. We developed a novel method to quantitatively analyze cardiac deformation and motion from echo sequences. Our estimated cardiac motion is not only regularized to be spatially but also temporally smooth. We validate our methods using (1) simulated echo images with known ground truth, and (2) in vivo echo images acquired on open-chests pigs with sonomicrometry. Tests indicate that our method can estimate cardiac motion more accurately than methods without temporal regularization.
Shared control for motion compensation in robotic beating heart surgery
2013 IEEE International Conference on Robotics and Automation, 2013
This paper presents a shared control approach for motion compensation in robotic beating heart surgery. Motion compensation consists of three main tasks; motion synchronization, image stabilization and shared control. The paper discusses a unifying framework under which the three tasks combine seamlessly. In this work, the planar 1-manifold case is considered, where a strip-wise affine map is performed to achieve image stabilization onto a canonical space, where shared control emerges naturally. A prototype teleoperation system is also described, implementing the algorithms. Experiments were performed with medically trained users, and the positive effect of motion compensation is analyzed.
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 surfacea 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 threedegree-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.