Elastographic image reconstruction: A stochastic state space approach

Model-based reconstruction algorithms have shown potentials over conventional strain-based methods in static elas-tographic image by using ”accurate” finite element(FE) or bio-mechanical models. Strictly speaking, however, the measurement noise are always exists and thus do not meet basic assumptions of these algorithms. In addition, the difficulty in determining the proper system response model also greatly affects the quality of the reconstructed images. In this paper, we explore the usage of state space principles for the estimation of materials properties in elastographic imaging. The model-data discrepancy is modeled as uncertainties, i.e. Gaussian white noise, and the measurement noise is treated as another independent Gaussian white noise in the stochastic state space space, and an optimal estimation is computed of full displacement field and Young´s modulus simultaneously using an extended Kalman filter (EKF). The performance of the proposed framework is evaluated using phantom data and real data with favorable results.