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Sampled-data h ∞ filtering for robust kinematics estimation: applications to biomechanics-based cardiac image analysis.
Content Provider | CiteSeerX |
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Author | Shi, Pengcheng Tong, Shan Sinusas, Albert |
Abstract | A sampled-data H ∞ filtering strategy is proposed for the estimation of cardiac kinematic functions from periodic medical image sequences. Given the biomechanics-based myocardial dynamics, stochastic multi-frame filtering frameworks are constructed to deal with the parameter uncertainty of the biomechanical constraining model and the noisy nature of the imaging data in a coordinated fashion. As robustness is of paramount importance in cardiac wall motion estimation, especially for clinical applications, this mini-max H ∞ strategy is particulary powerful for real-world problems where the types and levels of model uncertainties and data disturbances are not available a priori. For the hybrid cardiac analysis system with continuous dynamics and discrete measurements, the state estimates are predicted according to the continuous-time state equation between observation time points, and then updated with the new measurements obtained at discrete time instants, yielding physically more meaningful and more accurate estimation results for the continuously evolving cardiac dynamics. The strategy is validated through synthetic data experiments to illustrate its advantages and on canine MR phase contrast images to show its clinical relevance. Index Terms — Robust kinematics estimation, sampled-data H ∞ filtering |
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Access Restriction | Open |
Subject Keyword | Sampled-data Filtering Paramount Importance Sampled-data Filtering Robust Kinematics Estimation Mini-max Strategy Hybrid Cardiac Analysis System Index Term Robust Kinematics Estimation Clinical Application Periodic Medical Image Sequence Cardiac Dynamic Real-world Problem Parameter Uncertainty Sampled-data Filtering Strategy Noisy Nature State Estimate Stochastic Multi-frame Filtering Framework Model Uncertainty Cardiac Wall Motion Estimation Accurate Estimation Result New Measurement Application Biomechanics-based Cardiac Image Analysis Discrete Time Instant Synthetic Data Experiment Observation Time Point Data Disturbance Cardiac Kinematic Function Biomechanics-based Myocardial Dynamic Continuous Dynamic Continuous-time State Equation Biomechanical Constraining Model Clinical Relevance Discrete Measurement Canine Mr Phase Contrast Image Coordinated Fashion |
Content Type | Text |