A first-order adjoint and a second-order hybrid method for an energy output least-squares elastography inverse problem of identifying tumor location

DOI10.1186/1687-2770-2013-263zbMath1292.35323OpenAlexW2040329619WikidataQ59304838 ScholiaQ59304838MaRDI QIDQ2252632

Akhtar A. Khan, Brian C. Winkler, Baasansuren Jadamba, Nathan D. Cahill, Miguel Sama

Publication date: 18 July 2014

Published in: Boundary Value Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1186/1687-2770-2013-263

zbMATH Keywords

regularizationinverse problemsparameter identificationsaddle point problemsmixed finite element methodtumor identificationnearly incompressible elasticityenergy output least squares

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Inverse problems for PDEs (35R30) Finite element, Rayleigh-Ritz and Galerkin methods for boundary value problems involving PDEs (65N30) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21) Inverse problems in optimal control (49N45)

Related Items (10)

An equation error approach for the elasticity imaging inverse problem for predicting tumor location ⋮ Iterative Methods for the Elastography Inverse Problem of Locating Tumors ⋮ An iteratively regularized stochastic gradient method for estimating a random parameter in a stochastic PDE. A variational inequality approach ⋮ A convex inversion framework for identifying parameters in saddle point problems with applications to inverse incompressible elasticity ⋮ Three optimization formulations for an inverse problem in saddle point problems with applications to elasticity imaging of locating tumor in incompressible medium ⋮ First-order and second-order adjoint methods for parameter identification problems with an application to the elasticity imaging inverse problem ⋮ Analyzing the role of the Inf-Sup condition for parameter identification in saddle point problems with application in elasticity imaging ⋮ A variational inequality based stochastic approximation for inverse problems in stochastic partial differential equations ⋮ Proximal Methods for the Elastography Inverse Problem of Tumor Identification Using an Equation Error Approach ⋮ A New Energy Inversion for Parameter Identification in Saddle Point Problems with an Application to the Elasticity Imaging Inverse Problem of Predicting Tumor Location

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