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Biomechanical Model Guided Dual Estimation of Myocardial Motion

Author: MengJie
Tutor: LiuHuaFeng
School: Zhejiang University
Course: Optical Engineering
Keywords: Left ventricle Finite elements method State space method Dual estimation extended Kalman Filter
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 14
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Abstract

Ischemic heart disease(IHD) severely endangers health of mankind.In the early stages, patients suffer from IHD are not conscious of the sickness because of no obvious symptoms. So, it’s much meaningful for early diagnosis of IHD. According to a large number of researches and experimental, abnormalities of motion and material of left ventricle are main indicators for IHD. Strain and elastic modulus are two parameters that can reflect the motion state and material property of myocardium. By quantitative analysis of these two parameters, we can have better diagnosis for IHD.At present there are two kinds of method for strain imaging and elastic imaging. One is based on hardware of imaging; the other is based on model technique. Because of the inherent drawback of each method of heart imaging technique, there are relevant defects for hardware based method. The majority of traditional model techniques deal with cardiac motion analysis and material parameters estimation separately without consideration of their underlying close connection. So there are limitations for traditional model based method.In this paper, we present a new method of estimating deformation and material properties based on a biomechanical guided dual filter framework, which grows out our earlier joint estimation. This approach leads to fast convergence and avoid cross covariance between parameter and state, which are main drawbacks of joint estimation method. In our current implementation, at each time step, we rely on techniques from Kaman filter to first generate estimates of heart kinematics with suboptimal material parameter estimates, and then recover the elasticity property given these kinematic state estimates based on an extended Kalman filter(EKF), unscented Kalman filter(UKF) or traditional Kalman filter(KF) techniques according to different measure equations in parameter filter. These coupled iterative steps are repeated as necessary until convergence.In addition, we proposed a new approach of computation of the force that drives the moving heart. We use the difference method to solve the equations of Navies-Stokes from fluid mechanics by giving velocity of all point of myocardium. We try to insert this estimation force into our optimization framework, in order to improve the robust and accuracy of estimation results.

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