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

Franke, Timothy J.; Bebek, Özkan; Çavuşoğlu, M. Cenk

doi:10.1109/iros.2007.4399461

Cited by 30 publications

(37 citation statements)

References 6 publications

(13 reference statements)

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“…They reported RMS errors of 1.43 mm. Frank et al used adaptive filters to follow pre-recorded heart motion [18]. This study reported RMS errors of 0.5 mm.…”

Section: Resultsmentioning

confidence: 89%

“…ImageRobot Predictive Control Based Dynamics [1] Prediction No No [7] Prediction No Yes [11] Prediction Yes No [18] Prediction No Yes [19] Predictive Control No Yes [20] Predictive Control No Yes Proposed Predictive Yes Yes Method Control position, and the intervening dynamics between the reference position and the actual position, which is that of a voice coil linear actuator, is neglected. Other prediction methods address heart rate variability through the use of adaptive filters, which slowly change the length of the predicted heart beat to make it coincide with the length of the actual heartbeat.…”

Section: Prediction Ormentioning

confidence: 99%

“…Other prediction methods address heart rate variability through the use of adaptive filters, which slowly change the length of the predicted heart beat to make it coincide with the length of the actual heartbeat. In [18], the heart's position was captured with sonomicrometry crystals, not with medical images.…”

Section: Prediction Ormentioning

confidence: 99%

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Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery

Bowthorpe

Tavakoli

Becher

et al. 2014

IEEE J. Biomed. Health Inform.

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Abstract-Performing surgery on fast-moving heart structures while the heart is freely beating is next to impossible. Nevertheless, the ability to do this would greatly benefit patients. By controlling a teleoperated robot to continuously follow the heart's motion, the heart can be made to appear stationary. The surgeon will then be able to operate on a seemingly stationary heart when in reality it is freely beating. The heart's motion is measured from ultrasound images and thus involves a non-negligible delay due to image acquisition and processing, estimated to be 150 ms that, if not compensated for, can cause the teleoperated robot's end-effector (i.e., the surgical tool) to collide with and puncture the heart. This research proposes the use of a Smith predictor to compensate for this time delay in calculating the reference position for the teleoperated robot. The results suggest that heart motion tracking is improved as the introduction of the Smith predictor significantly decreases the mean absolute error, which is the error in making the distance between the robot's endeffector and the heart follow the surgeon's motion, and the mean integrated square error.

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“…They reported RMS errors of 1.43 mm. Frank et al used adaptive filters to follow pre-recorded heart motion [18]. This study reported RMS errors of 0.5 mm.…”

Section: Resultsmentioning

confidence: 89%

Section: Prediction Ormentioning

confidence: 99%

See 1 more Smart Citation

Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery

Bowthorpe

Tavakoli

Becher

et al. 2014

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

show abstract

“…Frank et al proposed a new Autoregressive (AR) model based adaptive prediction technique [8,9] that is less susceptible to the variation of the heart motion period and robust to noise. However, this AR model is still a linear model that cannot describe some of the nonlinear dynamics of heart motion.…”

Section: Related Workmentioning

confidence: 99%

Heart Motion Prediction in Robotic-Assisted Beating Heart Surgery: A Nonlinear Fast Adaptive Approach

Wang

et al. 2013

International Journal of Advanced Robotic Systems

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Off-pump Coronary Artery Bypass Graft (CABG) surgery outperforms traditional on-pump surgery because the assisted robotic tools can alleviate the relative motion between the beating heart and robotic tools. Therefore, it is possible for the surgeon to operate on the beating heart and thus lessens post surgery complications for the patients. Due to the highly irregular and non-stationary nature of heart motion, it is critical that the beating heart motion is predicted in the modelbased track control procedures. It is technically preferable to model heart motion in a nonlinear way because the characteristic analysis of 3D heart motion data through Bi-spectral analysis and Fourier methods demonstrates the involved nonlinearity of heart motion. We propose an adaptive nonlinear heart motion model based on the Volterra Series in this paper. We also design a fast lattice structure to achieve computational-efficiency for realtime online predictions. We argue that the quadratic term of the Volterra Series can improve the prediction accuracy by covering sharp change points and including the motion with sufficient detail. The experiment results indicate that the adaptive nonlinear heart motion prediction algorithm outperforms the autoregressive (AR) and the time-varying Fourier-series models in terms of the root mean square of the prediction error and the prediction error in extreme cases.

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“…Prior research in motion compensated coronary artery bypass graft has investigated prediction using an adaptive filter bank [4], an estimator based on Takens theorem [8], and a vector autoregressive least squares estimator [9]. In this work, we employ an extended Kalman filter (EKF) with an explicity quasiperiodic model, which is effective for 3DUS-guided mitral valve motion compensation [7].…”

Section: Quasiperiodic Predictive Filter: Extended Kalman Filtermentioning

confidence: 99%

3D Ultrasound-Guided Motion Compensation System for Beating Heart Mitral Valve Repair

Yuen

Kesner

Vasilyev

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. Beating heart intracardiac procedures promise significant benefits for patients, however, the fast motion of the heart poses serious challenges to surgeons. We present a new 3D ultrasound-guided motion (3DUS) compensation system that synchronizes instrument motion with the heart. The system utilizes the fact that the motion of some intracardiac structures, including the mitral valve annulus, is largely constrained to translation along one axis. This allows the development of a real-time 3DUS tissue tracker which we integrate with a 1 degree-of-freedom actuated surgical instrument, real-time 3DUS instrument tracker, and predictive filter to devise a system with synchronization accuracy of 1.8 mm RMSE. User studies involving the deployment of surgical anchors in a simulated mitral annuloplasty procedure demonstrate that the system increases success rates by over 100%. Furthermore, it enables more careful anchor deployment by reducing forces to the tissue by 50% while allowing instruments to remain in contact with the tissue for longer periods.

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

Cited by 30 publications

References 6 publications

Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery

Smith Predictor-Based Robot Control for Ultrasound-Guided Teleoperated Beating-Heart Surgery

Heart Motion Prediction in Robotic-Assisted Beating Heart Surgery: A Nonlinear Fast Adaptive Approach

3D Ultrasound-Guided Motion Compensation System for Beating Heart Mitral Valve Repair

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