Based On Left Ventricular Motion And Material Properties Of The Image Sequence Analysis

Medical imaging technologies play an important role in the help diagnosis of disease. Quantitative and noninvasive estimation of cardiac material properties and regional kinematic functions from image sequences has significant clinical and physiological implications.In order to quantify Young's modulus of myocardium--the most important material parameter from the elastic mechanics' point of view, a new modulus reconstruction algorithm based on meshfree method and Kalman filter is proposed. With known nodal displacements and force boundaries, modulus is calculated using inverse meshfree technique and Kalman filter. The advantage of the node-based meshfree method is that there is no need to consider the nodal connectivities. Besides, it is convenient to alter the distribution and sampling of nodes as well as the interpolating function. Synthetic data is used to evaluate the performance of the algorithm.While existing biomechanical studies of the myocardial material constitutive laws have assumed known tissue kinematic measurements, and image analysis efforts on cardiac kinematic functions have relied on fixed constraining models of mathematical or mechanical nature, we believe that a probabilistic strategy is needed to achieve robust and optimal estimates of kinematics functions and material parameters at the same time, both of which are treated as random variables with know statistics. A stochastic finite element framework is presented for the simultaneous estimation of the cardiac kinematic functions and material model parameters from periodic medical image sequences. After the conversion of the cardiac dynamic equation to state space representation, extended Kalman filter is employed to perform the joint estimation in an approximate optimal sense. This framework is validated by both synthetic data and image data. Results from human MRI image sequences are presented in the end.