Recovery of Nonrigid Motion and Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bending and stretching models for LV wall motion analysis from curves and surfaces
Image and Vision Computing - Special issue: information processing in medical imaging 1991
Dense Non-Rigid Motion Estimation in Sequencesof Medical Images Using Differential Constraints
International Journal of Computer Vision
Medical Image Analysis: Progress over Two Decades and the Challenges Ahead
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
Modal Matching for Correspondence and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
In-vivo Strain and Stress Estimation of the Left Ventricle from MRI Images
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
Motion Analysis of the Right Ventricle From MRI Images
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Computational Complexity Reduction for Volumetric Cardiac Deformation Recovery
Journal of Signal Processing Systems
Physiome model based state-space framework for cardiac kinematics recovery
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Cardiac motion recovery: continuous dynamics, discrete measurements, and optimal estimation
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
State-Space Analysis of Cardiac Motion With Biomechanical Constraints
IEEE Transactions on Image Processing
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A time-domain filtering algorithm is proposed to recover myocardial kinematic function using output-only measurements without the time history of external loads. The main contribution of this work is that the overall effect of all the external loads on the myocardium is treated as a random variable disturbed by the Gaussian white noise because the external loads of the myocardium are usually unknown in practical exercises. The kernel of our proposed algorithm is an iterative, multiframe, and sequential filtering procedure consisting of a Kalman filter and a least-squares filter. In our proposed implementation, the initial guess of myocardial kinematic function and residual innovation of all the state variables are first computed using a Kalman filter via state space equations only driven by the Gaussian white noise, and then the residual innovation is fed into a least-squares filter to estimate the total external loads of themyocardium. In the end, the initial guess of myocardial kinematic function is corrected using external loads provided by the least-squares filter. After the introduction of the whole structure of our algorithm, we demonstrate the ability of the framework on synthetic data and MR image sequences.