Model-based Motion Estimation of Elastic Surfaces for Minimally Invasive Cardiac Surgery

Bader, Thomas K.; Wiedemann, Albert; Roberts, Kathrin; Hanebeck, Uwe D.

doi:10.1109/robot.2007.363656

Cited by 32 publications

(34 citation statements)

References 13 publications

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“…The individual sensor nodes deployed in the environment provide distributed measurements in an intelligent and autonomous manner [1], [2] and collect information about the phenomenon to be monitored. Examples of distributed phenomena to be reconstructed include temperature distributions in a plane, fluid flows, deflection of bearings, and the surface motion of a beating heart in minimally invasive surgery [3].…”

Section: Introductionmentioning

confidence: 99%

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Sawo

Huber

Hanebeck

2007

2007 10th International Conference on Information Fusion

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Abstract-This paper addresses the problem of model-based reconstruction and parameter identification of distributed phenomena characterized by partial differential equations. The novelty of the proposed method is the systematic approach and the integrated treatment of uncertainties, which naturally occur in the physical system and arise from noisy measurements. The main challenge of accurate reconstruction is that model parameters, i.e., diffusion coefficients, of the physical model are not known in advance and usually need to be identified. Generally, the problem of parameter identification leads to a nonlinear estimation problem. Hence, a novel efficient recursive procedure is employed. Unlike other estimators, the so-called Hybrid Density Filter not only assures accurate estimation results for nonlinear systems, but also offers an efficient processing. By this means it is possible to reconstruct and identify distributed phenomena monitored by autonomous wireless sensor networks. The performance of the proposed estimation method is demonstrated by means of simulations.

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Section: Introductionmentioning

confidence: 99%

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Sawo

Huber

Hanebeck

2007

2007 10th International Conference on Information Fusion

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“…In [17], a physical heart surface model in the form of a thin membrane is proposed for intraoperative tracking of the heart motion. The motion in consideration is greatly simplified by assuming that the heart moves only in out-of-plane direction.…”

Section: Related Workmentioning

confidence: 99%

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

2010

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Purpose Tracking of beating heart motion in a robotic surgery system is required for complex cardiovascular interventions. Methods A heart surface motion tracking method is developed, including a stochastic physics-based heart surface model and an efficient reconstruction algorithm. The algorithm uses the constraints provided by the model that exploits the physical characteristics of the heart. The main advantage of the model is that it is more realistic than most standard heart models. Additionally, no explicit matching between the measurements and the model is required. The application of meshless methods significantly reduces the complexity of physics-based tracking. Results Based on the stochastic physical model of the heart surface, this approach considers the motion of the intervention area and is robust to occlusions and reflections. The tracking algorithm is evaluated in simulations and experiments on an artificial heart. Providing higher accuracy than the standard model-based methods, it copes successfully with occlusions and provides high performance when all measurements are not available. Conclusions Combining the physical and stochastic description of the heart surface motion ensures physically correct and accurate prediction. Automatic initialization of the physics-based cardiac motion tracking enables system evaluation in a clinical environment.

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“…In contrast to other approaches, the physics-based tracking [3], [4] guarantees a physically correct prediction of the heart behavior. This tracking is based on a physical heart model and thus, exploits additional background information about the physical characteristics of the heart.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous state and parameter estimation for physics-based tracking of heart surface motion

Bogatyrenko

Hanebeck

2010

2010 IEEE Conference on Multisensor Fusion and Integration

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Abstract-Most existing approaches for tracking of the beating heart motion assume known cardiac kinematics and material parameters. However, these assumptions are not realistic for application in beating heart surgery. In this paper, a novel probabilistic tracking approach based on a physical model of the heart surface is presented. In contrast to existing approaches, the physical information about heart kinematics and material properties is incorporated and considered in an estimation of the heart behavior. An additional advantage is that the time-dependencies and uncertainties of the heart parameters are efficiently handled by exploiting simultaneous state and parameter estimation. Furthermore, by decomposing the state into linear and nonlinear substructures, the computational complexity of the estimation problem is reduced. The experimental results demonstrate the high performance of the method proposed in this paper. The solution of the parameter identification problem allows a personalized physical model and opens up possibilities to apply the physics-based tracking of the heart surface motion in a clinical environment.

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Model-based Motion Estimation of Elastic Surfaces for Minimally Invasive Cardiac Surgery

Cited by 32 publications

References 13 publications

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Parameter identification and reconstruction for distributed phenomena based on hybrid density filter

Efficient physics-based tracking of heart surface motion for beating heart surgery robotic systems

Simultaneous state and parameter estimation for physics-based tracking of heart surface motion

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