Cardiac Motion Estimation

This research involves multiple studies for cardiac motion estimation and point-of-interest (POI) localization on cardiac surface 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, was found to be necessary.

For this purpose, we have demonstrated the effectiveness of a receding horizon model predictive control-based algorithm, which used adaptive filter based predictors, under constant and slowly varying heart rate conditions as well as the cases when the heart motion statistics change abruptly and significantly, such as during arrhythmias. However, performance of these feed-forward motion control algorithms under heart rhythm variations is an important concern.

Feasibility studies are carried out to assess the motion tracking capabilities of the adaptive algorithms. Specifically, the tracking performance of the algorithms is evaluated on prerecorded motion data, which is collected in vivo. 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.

Another critical component of robotic-assisted beating heart surgery is precise localization of a 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.

For this purpose, we invetigated 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 adaptive predictors. The proposed methods are validated with prerecorded in-vivo heart motion data.

Article

• Localization of Point-of-Interest Positions on Cardiac Surface for Robotic-Assisted Beating Heart Surgery
  E. E. Tuna and M. C. Cavusoglu
  Conf Proc IEEE Eng Med Biol Soc (EMBC), 2021, doi.org/10.1109/EMBC46164.2021.9630917. [pdf]

• Towards Active Tracking of Beating Heart Motion in the Presence of Arrhythmia for Robotic Assisted Beating Heart Surgery
  E. E. Tuna., J. H. Karimov, T. Liu, O. Bebek, K. Fukamachi, and M. C. Cavusoglu
  PLOS One, 9 (7), 2014, doi.org/10.1371/journal.pone.0102877. [pdf]

• Heart Motion Prediction Based on Adaptive Estimation Algorithms for Robotic-Assisted Beating Heart Surgery
  E. E. Tuna, T. J. Franke, O. Bebek, A. Shiose, K. Fukamachi, and M. C. Cavusoglu
  IEEE Trans Robot, 29 (1), 2013, doi.org/10.1109/TRO.2012.2217676. [pdf]