Robotic Assisted Beating Heart Surgery Research Articles

Localization of Point-of-Interest Positions on Cardiac Surface for Robotic-Assisted Beating Heart Surgery.

One of the critical components of robotic-assisted beating heart surgery is precise localization of a point-of-interest (POI) position on cardiac surface, which needs to be tracked by the robotic instruments. This is challenging as the incoming sensor measurements, from which POI position is localized, might be noisy and incomplete. This paper presents two Bayesian filtering based localization approaches to localize POI position online from sonomicrometer measurements. Specifically, extended Kalman filter (EKF) and particle filter (PF) localization algorithms are explored to estimate the state of POI position. The estimations of upcoming heart motion generated by the generalized adaptive predictor, which is demonstrated in the authors' past work, are also incorporated to generate an improved motion model. The proposed methods are validated with prerecorded in-vivo heart motion data.

A comprehensive multimodality heart motion prediction algorithm for robotic-assisted beating heart surgery.

An essential requirement for performing robotic-assisted surgery on a freely beating heart is a prediction algorithm that can estimate the future heart trajectory. Heart motion, respiratory volume (RV) and electrocardiogram (ECG) signal were measured from two dogs during thoracotomy surgery. A comprehensive multimodality prediction algorithm was developed based on the multivariate autoregressive model to incorporate the heart trajectory and cardiorespiratory data with multiple inherent measurement rates explicitly. Experimental results indicated strong relationships between the dominant frequencies of heart motion with RV and ECG. The prediction algorithm revealed a high steady state accuracy, with the root mean square (RMS) errors in the range of 82 to 162μm for a 300-second interval, less than half of that of the best competitor. The proposed multimodality prediction algorithm is promising for practical use in robotic assisted beating heart surgery, considering its capability of providing highly accurate predictions in long horizons.

A Hybrid Algorithm for Prediction of Varying Heart Rate Motion in Computer-Assisted Beating Heart Surgery.

An essential requirement for performing robotic assisted surgery on a freely beating heart is a prediction algorithm which can estimate the future trajectory of the heart in the varying heart rate (HR) conditions of real surgery with a high accuracy. In this study, a hybrid amplitude modulation- (AM) and autoregressive- (AR) based algorithm was developed to enable estimating the global and local oscillations of the beating heart, raised from its major and minor physiological activities. The AM model was equipped with an estimator of the heartbeat frequency to compensate for the HR variations. The RMS of the prediction errors of the hybrid algorithm was in the range of 165-361μm for the varying HR motion, 21% less than that of the single AM model. With the capability of providing highly accurate predictions in a wide range of HR variation, the hybrid model is promising for practical use in robotic assisted beating heart surgery.

Feasibility of infrared tracking of beating heart motion for robotic assisted beating heart surgery.

Accurate tracking of the heart surface motion is a major requirement for robot assisted beating heart surgery. The feasibility of a stereo infrared tracking system for measuring the free beating heart motion was investigated by experiments on a heart motion simulator, as well as model surgery on a dog. Simulator experiments revealed a high tracking accuracy (81 μm root mean square error) when the capturing times were synchronized and the tracker pointed at the target from a 100 cm distance. The animal experiment revealed the applicability of the infrared tracker with passive markers in practical heart surgery conditions. With the current technology, infrared tracking with passive markers might be the optimal solution for accurate, fast, and reliable tracking of heart motion during robot assisted beating heart surgery.

Towards active tracking of beating heart motion in the presence of arrhythmia for robotic assisted beating heart surgery.

In robotic assisted beating heart surgery, the control architecture for heart motion tracking has stringent requirements in terms of bandwidth of the motion that needs to be tracked. In order to achieve sufficient tracking accuracy, feed-forward control algorithms, which rely on estimations of upcoming heart motion, have been proposed in the literature. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern. In their past work, the authors have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used generalized adaptive predictors, under constant and slowly varying heart rate conditions. This paper extends these studies to the case when the heart motion statistics change abruptly and significantly, such as during arrhythmias. A feasibility study is carried out to assess the motion tracking capabilities of the adaptive algorithms in the occurrence of arrhythmia during beating heart surgery. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected in vivo and includes heart rhythm irregularities. The algorithms are tested using both simulations and bench experiments on a three degree-of-freedom robotic test bed. They are also compared with a position-plus-derivative controller as well as a receding horizon model predictive controller that employs an extended Kalman filter algorithm for predicting future heart motion.

Multivariate vector autoregressive prognosis-based model following control method for robot-assisted beating heart surgery

Robotics tool enables the surgeon to conduct the operation on the beating heart during the off-pump coronary artery graft bypass surgery. The robotic tool actively cancels the relative motion between the point of intersect (POI) on the surface of the heart and surgical tool, which allows the surgeon to operate as if the heart is stationary. The nonlinear nature of the beating heart motion disables the conventional feedback controller and pose difficulty to the robot tool. We apply Granger causality to analyze simultaneously measured electrocardiography (ECG) and 3D heart motion data. The extracted interdependency between the different sets of time series reveals the feasiblity for the improved prediction of the heart motion. In this paper, we propose an adaptive multivariate vector autoregressive (MVAR) prognosis-based model following (MF) control algorithm. Using this method, the prediction part takes advantage of the ECG signal, which contains the nonstationary heart rate dynamics significantly correlated with the heart motion. In addition, the control part automatically incorporates the feedforward path to enhance the tracking of the abrupt change and occasional abnormality in heart motion. The comparative experiment results for evaluating the proposed algorithm are reported by using the vivo collected data. The results indicate that MVAR-based MF improves the control accuracy by 0.2 mm and achieves better tracking performance by capturing more nonlinear characteristics of the heart motion, and following the heart motion better with sufficient details.

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 (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 motion over a prediction horizon in order to achieve sufficient tracking accuracy. In this paper, two least-square 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 3 degrees of freedom 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.

A Quadratic Nonlinear Prediction-Based Heart Motion Model following Control Algorithm in Robotic-Assisted Beating Heart Surgery

Off-pump coronary artery bypass graft surgery outperforms the traditional on-pump surgery because the assisted robotic tools can cancel the relative motion between the beating heart and the robotic tools, which reduces post-surgery complications for patients. The challenge for the robot assisted tool when tracking the beating heart is the abrupt change caused by the nonlinear nature of heart motion and high precision surgery requirements. A characteristic analysis of 3D heart motion data through bi-spectral analysis demonstrates the quadratic nonlinearity in heart motion. Therefore, it is necessary to introduce nonlinear heart motion prediction into the motion tracking control procedures. In this paper, the heart motion tracking problem is transformed into a heart motion model following problem by including the adaptive heart motion model into the controller. Moreover, the model following algorithm with the nonlinear heart motion model embedded inside provides more accurate future reference by the quadratic term of sinusoid series, which could enhance the tracking accuracy of sharp change point and approximate the motion with sufficient detail. The experiment results indicate that the proposed algorithm outperforms the linear prediction-based model following controller in terms of tracking accuracy (root mean square).

Three-dimensional heart motion estimation using endoscopic monocular vision system: From artificial landmarks to texture analysis

In robot-assisted beating heart surgery, motion of the heart surface might be virtually stabilized to let the surgeon work as in on-pump cardiac surgery. Virtual stabilization means to compensate physically the relative motion between the instrument tool tip and the region of interest on the heart surface, and to offer surgeon a stable visual display of the scene. To this end, motion of the heart must be estimated. This article focusses on motion estimation of the heart surface. Two approaches are considered in the paper. The first one is based on landmark tracking allowing 3D pose estimation. The second is based on texture tracking. Classical computer vision methods, as well as a new texture-based tracking scheme has been applied to track the heart motion, and, when possible, reconstruct 3D distance to the heart surface. Experimental results obtained on in vivo images show the estimated motion of heart surface points.

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.

3D HEART MOTION ESTIMATION USING ENDOSCOPIC MONOCULAR VISION SYSTEM

In robotic assisted beating heart surgery, motion of the heart surface might be virtually stabilized to let the surgeon work as in on-pump cardiac surgery. Virtual stabilization means to compensate physically the relative motion between instrument tool tip and point of interest on the heart surface, and to offer surgeon a stable visual display of the scene. In this way, motion of the heart must be estimated. This article focus on motion estimation of heart surface. Classical computer vision method has been applied to reconstruct 3D pose of interest point. Experimental results obtained on in vivo images show the estimated motion of heart surface points.