Heart Motion Estimation Research Articles

Beating-heart robotic surgery using bilateral impedance control: Theory and experiments

Abstract A bilateral impedance controller is presented to enable robot-assisted surgery of a beating heart. For this purpose, two desired impedance models are designed and realized for the master and slave robots interacting with the operator (surgeon) and the environment (heart tissue), respectively. The impedance models are designed such that (a) the slave robot complies with the oscillatory motion of the beating heart and (b) the surgeon perceives the non-oscillatory portion of the slave/heart contact force at the master robot implying arrested-heart surgery. These performance goals are achieved via appropriate adjustment of the impedance model parameters without any measurement or estimation of heart motion. Two nonlinear robust adaptive controllers are proposed for the master and slave robots to track their corresponding desired impedance responses in the Cartesian space. The stability, tracking convergence and the robustness against parametric and non-parametric modeling uncertainties are proven using the Lyapunov theorem and based on two types of adaptation laws. The stability of impedance models and nonlinear tele-operation system can enhance the patient’s safety during the robotic surgery. Experimental results show that the proposed controller compensates for the beating motion and provides smooth force feedback to the surgeon.

Sliding to predict: vision-based beating heart motion estimation by modeling temporal interactions

PurposeTechnical advancements have been part of modern medical solutions as they promote better surgical alternatives that serve to the benefit of patients. Particularly with cardiovascular surgeries, robotic surgical systems enable surgeons to perform delicate procedures on a beating heart, avoiding the complications of cardiac arrest. This advantage comes with the price of having to deal with a dynamic target which presents technical challenges for the surgical system. In this work, we propose a solution for cardiac motion estimation.MethodsOur estimation approach uses a variational framework that guarantees preservation of the complex anatomy of the heart. An advantage of our approach is that it takes into account different disturbances, such as specular reflections and occlusion events. This is achieved by performing a preprocessing step that eliminates the specular highlights and a predicting step, based on a conditional restricted Boltzmann machine, that recovers missing information caused by partial occlusions.ResultsWe carried out exhaustive experimentations on two datasets, one from a phantom and the other from an in vivo procedure. The results show that our visual approach reaches an average minima in the order of magnitude of 10^{-7} while preserving the heart’s anatomical structure and providing stable values for the Jacobian determinant ranging from 0.917 to 1.015. We also show that our specular elimination approach reaches an accuracy of 99% compared to a ground truth. In terms of prediction, our approach compared favorably against two well-known predictors, NARX and EKF, giving the lowest average RMSE of 0.071.ConclusionOur approach avoids the risks of using mechanical stabilizers and can also be effective for acquiring the motion of organs other than the heart, such as the lung or other deformable objects.

Speckle generation and analysis of speckle tracking performance in a multi-scatter pressure field

Speckle tracking imaging is used as a method to estimate heart strain. An analysis of accuracy of speckle tracking and its potential to be utilized in quantification of myocardial stress through estimation of heart motion is examined. Multiple scattering effects are modeled using the Kirchoff integral formulation for the pressure field. The method of Pade approximants is used to accelerate convergence and to obtain temporal varying characteristics of the scattered field. Phantoms having varied acoustical contrast media and speckle density are used in this study. The effectiveness of inter-image frame of correlation methods for estimating speckle motion in high contrast media is considered. (NSF Grant 0841392)

Model based heart motion estimation for robotic surgery

Objectives: In order to synchronize robotic surgical instruments during totally endoscopic coronary bypass grafting with the heart motion the estimation of the position of the intervention point is required. In this study a model-based heart motion estimator is presented and validated in beating artificial heart model.

Cardiac C-Arm CT: A Unified Framework for Motion Estimation and Dynamic CT

Generating 3-D images of the heart during interventional procedures is a significant challenge. In addition to real time fluoroscopy, angiographic C-arm systems can also now be used to generate 3-D/4-D CT images on the same system. One protocol for cardiac CT uses ECG triggered multisweep scans. A 3-D volume of the heart at a particular cardiac phase is then reconstructed by applying Feldkamp (FDK) reconstruction to the projection images with retrospective ECG gating. In this work we introduce a unified framework for heart motion estimation and dynamic cone-beam reconstruction using motion corrections. The benefits of motion correction are 1) increased temporal and spatial resolution by removing cardiac motion which may still exist in the ECG gated data sets and 2) increased signal-to-noise ratio (SNR) by using more projection data than is used in standard ECG gated methods. Three signal-enhanced reconstruction methods are introduced that make use of all of the acquired projection data to generate a 3-D reconstruction of the desired cardiac phase. The first averages all motion corrected back-projections; the second and third perform a weighted averaging according to 1) intensity variations and 2) temporal distance relative to a time resolved and motion corrected reference FDK reconstruction. In a comparison study seven methods are compared: nongated FDK, ECG-gated FDK, ECG-gated, and motion corrected FDK, the three signal-enhanced approaches, and temporally aligned and averaged ECG-gated FDK reconstructions. The quality measures used for comparison are spatial resolution and SNR. Evaluation is performed using phantom data and animal models. We show that data driven and subject-specific motion estimation combined with motion correction can decrease motion-related blurring substantially. Furthermore, SNR can be increased by up to 70% while maintaining spatial resolution at the same level as is provided by the ECG-gated FDK. The presented framework provides excellent image quality for cardiac C-arm CT.