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

Genomic insights into heart health: Exploring the genetic basis of cardiovascular disease

Cardiovascular diseases (CVDs) are considered as the leading cause of death worldwide. CVD continues to be a major cause of death and morbidity despite significant improvements in its detection and treatment. Therefore, it is strategically important to be able to precisely characterize an individual's sensitivity to certain illnesses. The discovery of genes linked to cardiovascular illnesses has benefited from linkage analysis and genome-wide association research. The last 20 years have seen significant advancements in the field of molecular genetics, particularly with the development of new tools like genome-wide association studies. In this article we explore the profound impact of genetic variations on disease development, prognosis, and therapeutic responses. And the significance of genetics in cardiovascular risk assessment and the ever-evolving realm of genetic testing, offering insights into the potential for personalized medicine in this domain. Embracing the future of cardiovascular care, the article explores the implications of pharmacogenomics for tailored treatments, the promise of emerging technologies in cardiovascular genetics and therapies, including the transformative influence of nanotechnology. Furthermore, it delves into the exciting frontiers of gene editing, such as CRISPR/Cas9, as a novel approach to combat cardiovascular diseases. And also explore the potential of stem cell therapy and regenerative medicine, providing a holistic view of the dynamic landscape of cardiovascular genomics and its transformative potential for the field of cardiovascular medicine.

Deformable cardiac surface tracking by adaptive estimation algorithms

This study presents a particle filter based framework to track cardiac surface from a time sequence of single magnetic resonance imaging (MRI) slices with the future goal of utilizing the presented framework for interventional cardiovascular magnetic resonance procedures, which rely on the accurate and online tracking of the cardiac surface from MRI data. The framework exploits a low-order parametric deformable model of the cardiac surface. A stochastic dynamic system represents the cardiac surface motion. Deformable models are employed to introduce shape prior to control the degree of the deformations. Adaptive filters are used to model complex cardiac motion in the dynamic model of the system. Particle filters are utilized to recursively estimate the current state of the system over time. The proposed method is applied to recover biventricular deformations and validated with a numerical phantom and multiple real cardiac MRI datasets. The algorithm is evaluated with multiple experiments using fixed and varying image slice planes at each time step. For the real cardiac MRI datasets, the average root-mean-square tracking errors of 2.61 mm and 3.42 mm are reported respectively for the fixed and varying image slice planes. This work serves as a proof-of-concept study for modeling and tracking the cardiac surface deformations via a low-order probabilistic model with the future goal of utilizing this method for the targeted interventional cardiac procedures under MR image guidance. For the real cardiac MRI datasets, the presented method was able to track the points-of-interests located on different sections of the cardiac surface within a precision of 3 pixels. The analyses show that the use of deformable cardiac surface tracking algorithm can pave the way for performing precise targeted intracardiac ablation procedures under MRI guidance. The main contributions of this work are twofold. First, it presents a framework for the tracking of whole cardiac surface from a time sequence of single image slices. Second, it employs adaptive filters to incorporate motion information in the tracking of nonrigid cardiac surface motion for temporal coherence.

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 Probabilistic Approach for Contact Stability and Contact Safety Analysis of Robotic Intracardiac Catheter

The disturbances caused by the blood flow and tissue surface motions are major concerns during the motion planning of an intracardiac robotic catheter. Maintaining a stable and safe contact on the desired ablation point is essential for achieving effective lesions during the ablation procedure. In this paper, a probabilistic formulation of the contact stability and the contact safety for intravascular cardiac catheters under the blood flow and surface motion disturbances is presented. Probabilistic contact stability and contact safety metrics, employing a sample-based representation of the blood flow velocity distribution and the heart motion trajectory, are introduced. Finally, the contact stability and safety for an magnetic resonance imaging-actuated robotic catheter under main pulmonary artery blood flow disturbances and left ventricle surface motion disturbances are analyzed in simulation as example scenarios.

Contact Stability and Contact Safety of a Magnetic Resonance Imaging-Guided Robotic Catheter Under Heart Surface Motion

Contact force quality is one of the most critical factors for safe and effective lesion formation during catheter based atrial fibrillation ablation procedures. In this paper, the contact stability and contact safety of a novel magnetic resonance imaging (MRI)-actuated robotic cardiac ablation catheter subject to surface motion disturbances are studied. First, a quasi-static contact force optimization algorithm, which calculates the actuation needed to achieve a desired contact force at an instantaneous tissue surface configuration is introduced. This algorithm is then generalized using a least-squares formulation to optimize the contact stability and safety over a prediction horizon for a given estimated heart motion trajectory. Four contact force control schemes are proposed based on these algorithms. The first proposed force control scheme employs instantaneous heart position feedback. The second control scheme applies a constant actuation level using a quasi-periodic heart motion prediction. The third and the last contact force control schemes employ a generalized adaptive filter-based heart motion prediction, where the former uses the predicted instantaneous position feedback, and the latter is a receding horizon controller. The performance of the proposed control schemes is compared and evaluated in a simulation environment.

Review of Advanced Medical Telerobots

The advent of telerobotic systems has revolutionized various aspects of the industry and human life. This technology is designed to augment human sensorimotor capabilities to extend them beyond natural competence. Classic examples are space and underwater applications when distance and access are the two major physical barriers to be combated with this technology. In modern examples, telerobotic systems have been used in several clinical applications, including teleoperated surgery and telerehabilitation. In this regard, there has been a significant amount of research and development due to the major benefits in terms of medical outcomes. Recently telerobotic systems are combined with advanced artificial intelligence modules to better share the agency with the operator and open new doors of medical automation. In this review paper, we have provided a comprehensive analysis of the literature considering various topologies of telerobotic systems in the medical domain while shedding light on different levels of autonomy for this technology, starting from direct control, going up to command-tracking autonomous telerobots. Existing challenges, including instrumentation, transparency, autonomy, stochastic communication delays, and stability, in addition to the current direction of research related to benefit in telemedicine and medical automation, and future vision of this technology, are discussed in this review paper.

Contact Stability Analysis of Magnetically-Actuated Robotic Catheter Under Surface Motion

Contact force quality is one of the most critical factors for safe and effective lesion formation during cardiac ablation. The contact force and contact stability plays important roles in determining the lesion size and creating a gap-free lesion. In this paper, the contact stability of a novel magnetic resonance imaging (MRI)-actuated robotic catheter under tissue surface motion is studied. The robotic catheter is modeled using a pseudo-rigid-body model, and the contact model under surface constraint is provided. Two contact force control schemes to improve the contact stability of the catheter under heart surface motions are proposed and their performance are evaluated in simulation.

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

BACKGROUND

An essential requirement for performing robotic-assisted surgery on a freely beating heart is a prediction algorithm that can estimate the future heart trajectory.

METHOD

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.

RESULTS

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 162 μm for a 300-second interval, less than half of that of the best competitor.

CONCLUSION

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.

Organ-mounted robot localization via function approximation

BACKGROUND

Organ-mounted robots adhere to the surface of a mobile organ as a platform for minimally invasive interventions, providing passive compensation of physiological motion. This approach is beneficial during surgery on the beating heart. Accurate localization in such applications requires accounting for the heartbeat and respiratory motion. Previous work has described methods for modeling quasi-periodic motion of a point and registering to a static preoperative map. The existing techniques, while accurate, require several respiratory cycles to converge.

METHODS

This paper presents a general localization technique for this application, involving function approximation using radial basis function (RBF) interpolation.

RESULTS

In an experiment in the porcine model in vivo, the technique yields mean localization accuracy of 1.25 mm with a 95% confidence interval of 0.22 mm.

CONCLUSIONS

The RBF approximation provides accurate estimates of robot location instantaneously.

Nanotechnology, an alternative with promising prospects and advantages for the treatment of cardiovascular diseases

Cardiovascular diseases (CVDs) are one of the most important causes of mortality and affecting the health status of patients. At the same time, CVDs cause a huge health and economic burden to the whole world. Although a variety of therapeutic drugs and measures have been produced to delay the progress of the disease and improve the quality of life of patients, most of the traditional therapeutic strategies can only cure the symptoms and cannot repair or regenerate the damaged ischemic myocardium. In addition, they may bring some unpleasant side effects. Therefore, it is vital to find and explore new technologies and drugs to solve the shortcomings of conventional treatments. Nanotechnology is a new way of using and manipulating the matter at the molecular scale, whose functional organization is measured in nanometers. Because nanoscale phenomena play an important role in cell signal transduction, enzyme action and cell cycle, nanotechnology is closely related to medical research. The application of nanotechnology in the field of medicine provides an alternative and novel direction for the treatment of CVDs, and shows excellent performance in the field of targeted drug therapy and the development of biomaterials. This review will briefly introduce the latest applications of nanotechnology in the diagnosis and treatment of common CVDs.

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 165-361 μm 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.

Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

Physiological motion modeling for organ-mounted robots

BACKGROUND

Organ-mounted robots passively compensate heartbeat and respiratory motion. In model-guided procedures, this motion can be a significant source of information that can be used to aid in localization or to add dynamic information to static preoperative maps.

METHODS

Models for estimating periodic motion are proposed for both position and orientation. These models are then tested on animal data and optimal orders are identified. Finally, methods for online identification are demonstrated.

RESULTS

Models using exponential coordinates and Euler-angle parameterizations are as accurate as models using quaternion representations, yet require a quarter fewer parameters. Models which incorporate more than four cardiac or three respiration harmonics are no more accurate. Finally, online methods estimate model parameters as accurately as offline methods within three respiration cycles.

CONCLUSIONS

These methods provide a complete framework for accurately modelling the periodic deformation of points anywhere on the surface of the heart in a closed chest.

Model predictive control of a robotically actuated delivery sheath for beating heart compensation

Minimally invasive surgery (MIS) during cardiovascular interventions reduces trauma and enables the treatment of high-risk patients who were initially denied surgery. However, restricted access, reduced visibility and control of the instrument at the treatment locations limits the performance and capabilities of such interventions during MIS. Therefore, the demand for technology such as steerable sheaths or catheters that assist the clinician during the procedure is increasing. In this study, we present and evaluate a robotically actuated delivery sheath (RADS) capable of autonomously and accurately compensating for beating heart motions by using a model-predictive control (MPC) strategy. We develop kinematic models and present online ultrasound segmentation of the RADS that are integrated with the MPC strategy. As a case study, we use pre-operative ultrasound images from a patient to extract motion profiles of the aortic heart valve (AHV). This allows the MPC strategy to anticipate for AHV motions. Further, mechanical hysteresis in the steering mechanism is compensated for in order to improve tip positioning accuracy. The novel integrated system is capable of controlling the articulating tip of the RADS to assist the clinician during cardiovascular surgery. Experiments demonstrate that the RADS follows the AHV motion with a mean positioning error of 1.68 mm. The presented modelling, imaging and control framework could be adapted and applied to a range of continuum-style robots and catheters for various cardiovascular interventions.

Robust Automatic Feature Tracking on Beating Human Hearts for Minimally Invasive CABG Surgery

This paper presents a robust algorithm for automatic tracking of feature points on the human heart. The emphases and key contributions of the proposed algorithm are uniform distribution of the feature points and sustained tolerable tracking error. While in many methods in the literature, detection takes place independently from the tracking procedure, adopting a different approach, we selected a data-driven detection stage, which works based on the feedback from tracking results from the Lucas–Kanade (LK) tracking algorithm to avoid unacceptable tracking errors. To ensure a uniform spatial distribution of the total detected feature points for tracking, a cost function is employed using the simulated annealing optimizer, which prevents the newly detected points from accumulating near the previously located points or stagnant regions. Implementing the proposed algorithm on a real human heart dataset showed that the presented algorithm yields more robust tracking and improved motion reconstruction, compared with the other available methods. Furthermore, to predict the motion of feature points for handling short-term occlusions, a state space model is utilized, and thin-plate spline (TPS) interpolation was also employed to estimate motion of any arbitrary point on the heart surface.

Ultrasound-Based Image Guidance and Motion Compensating Control for Robot-Assisted Beating-Heart Surgery

Performing a surgical task on a beating heart requires superhuman skill as the surgeon must manually track the heart’s motion while performing a surgical task. However, the ability to operate on a beating heart would eliminate the need to use a mechanical stabilizer or arrest the heart and connect the patient to a heart-lung machine and would consequently eliminate their side effects. This work develops the image processing and control structure for an ultrasound-guided robot-assisted beating heart surgical system that will move the surgical tool tip in synchrony with the heart. This would allow the surgeon to operate through teleoperation on a virtually stabilized point on the heart. In developing this system, the position data acquired from ultrasound images is upsampled and predicted ahead to compensate for the image acquisition and processing delay. We present the results of a user task based on mitral valve annuloplasty performed under ultrasound guidance.

Focused Ultrasound and Lithotripsy

Shock wave lithotripsy has generally been a first choice for kidney stone removal. The shock wave lithotripter uses an order of microsecond pulse durations and up to a 100 MPa pressure spike triggered at approximately 0.5-2 Hz to fragment kidney stones through mechanical mechanisms. One important mechanism is cavitation. We proposed an alternative type of lithotripsy method that maximizes cavitation activity to disintegrate kidney stones using high-intensity focused ultrasound (HIFU). Here we outline the method according to the previously published literature (Matsumoto et al., Dynamics of bubble cloud in focused ultrasound. Proceedings of the second international symposium on therapeutic ultrasound, pp 290-299, 2002; Ikeda et al., Ultrasound Med Biol 32:1383-1397, 2006; Yoshizawa et al., Med Biol Eng Comput 47:851-860, 2009; Koizumi et al., A control framework for the non-invasive ultrasound the ragnostic system. Proceedings of 2009 IEEE/RSJ International Conference on Intelligent Robotics and Systems (IROS), pp 4511-4516, 2009; Koizumi et al., IEEE Trans Robot 25:522-538, 2009). Cavitation activity is highly unpredictable; thus, a precise control system is needed. The proposed method comprises three steps of control in kidney stone treatment. The first step is control of localized high pressure fluctuation on the stone. The second step is monitoring of cavitation activity and giving feedback on the optimized ultrasound conditions. The third step is stone tracking and precise ultrasound focusing on the stone. For the high pressure control we designed a two-frequency wave (cavitation control (C-C) waveform); a high frequency ultrasound pulse (1-4 MHz) to create a cavitation cloud, and a low frequency trailing pulse (0.5 MHz) following the high frequency pulse to force the cloud into collapse. High speed photography showed cavitation collapse on a kidney stone and shock wave emission from the cloud. We also conducted in-vitro erosion tests of model and natural kidney stones. For the model stones, the erosion rate of the C-C waveform showed a distinct advantage with the combined high and low frequency waves over either wave alone. For optimization of the high frequency ultrasound intensity, we investigated the relationship between subharmonic emission from cavitation bubbles and stone erosion volume. For stone tracking we have also developed a non-invasive ultrasound theragnostic system (NIUTS) that compensates for kidney motion. Natural stones were eroded and most of the resulting fragments were less than 1 mm in diameter. The small fragments were small enough to pass through the urethra. The results demonstrate that, with the precise control of cavitation activity, focused ultrasound has the potential to be used to develop a less invasive and more controllable lithotripsy system.

Identification and Active Exploration of Deformable Object Boundary Constraints through Robotic Manipulation

Robotic motion planning algorithms for manipulation of deformable objects, such as in medical robotics applications, rely on accurate estimations of object deformations that occur during manipulation. An estimation of the tissue response (for off-line planning or real-time on-line re-planning), in turn, requires knowledge of both object constitutive parameters and boundary constraints. In this paper, a novel algorithm for estimating boundary constraints of deformable objects from robotic manipulation data is presented. The proposed algorithm uses tissue deformation data collected with a vision system, and employs a multi-stage hill climbing procedure to estimate the boundary constraints of the object. An active exploration technique, which uses an information maximization approach, is also proposed to extend the identification algorithm. The effects of uncertainties on the proposed methods are analyzed in simulation. The results of experimental evaluation of the methods are also presented.

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.

Heart motion uncertainty compensation prediction method for robot assisted beating heart surgery - Master-slave Kalman Filters approach

Robot Assisted Coronary Artery Bypass Graft (CABG) allows the heart keep beating in the surgery by actively eliminating the relative motion between point of interest (POI) on the heart surface and surgical tool. The inherited nonlinear and diverse nature of beating heart motion gives a huge obstacle for the robot to meet the demanding tracking control requirements. In this paper, we novelty propose a Master-slave Kalman Filter based on beating heart motion Nonlinear Adaptive Prediction (NAP) algorithm. In the study, we describe the beating heart motion as the combination of nonlinearity relating mathematics part and uncertainty relating non-mathematics part. Specifically, first, we model the nonlinearity of the heart motion via quadratic modulated sinusoids and estimate it by a Master Kalman Filter. Second, we involve the uncertainty heart motion by adaptively change the covariance of the process noise through the slave Kalman Filter. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The results indicate that the new approach reduces prediction errors by at least 30 μm. Moreover, the new approach performs well in robustness test, in which two kinds of arrhythmia datasets from MIT-BIH arrhythmia database are assessed.

Heart motion measurement with three dimensional sonomicrometry and acceleration sensing

In robotic assisted beating heart surgery, the goal is to develop a robotic system that can actively cancel heart motion by closely following a point of interest (POI) on the heart surface, a process called Active Relative Motion Canceling (ARMC). In order to track and cancel POI motion precisely, control algorithms require good quality heart motion data. In this paper, a novel method is described which uses a particle filter to estimate the three-dimensional location of POI on heart surface by using measurements obtained from sonomicrometry along with an accelerometer. The new method employs a differential probability approach to increase the accuracy of the particle filter. The performance of the proposed method is evaluated by simulations.