Efficient implementations of a pseudodynamical stochastic filtering strategy for static elastography.
A computationally efficient pseudodynamical filtering setup is established for elasticity imaging (i.e., reconstruction of shear modulus distribution) in soft-tissue organs given statically recorded and partially measured displacement data. Unlike a regularized quasi-Newton method (QNM) that needs inversion of ill-conditioned matrices, the authors explore pseudodynamic extended and ensemble Kalman filters (PD-EKF and PD-EnKF) that use a parsimonious representation of states and bypass explicit regularization by recursion over pseudotime. Numerical experiments with QNM and the two filters suggest that the PD-EnKF is the most robust performer as it exhibits no sensitivity to process noise covariance and yields good reconstruction even with small ensemble sizes.
Banerjee
Biswanath
B
Department of Civil Engineering, Structures Laboratory, Indian Institute of Science, Bangalore 560012, India.
Roy
Debasish
D
Vasu
Ram Mohan
RM
D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D004548:Elasticity; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D033081:Shear Strength; D013269:Stochastic Processes