Incorporating structural prior information and sparsity into EIT using parallel level sets

DOI10.3934/ipi.2019015zbMath1410.65433OpenAlexW2910107980WikidataQ128549771 ScholiaQ128549771MaRDI QIDQ1737099

Simon R. Arridge, Ville Kolehmainen, Matthias J. Ehrhardt

Publication date: 26 March 2019

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/ipi.2019015

zbMATH Keywords

finite element methodregularizationelectrical impedance tomographystructural priorcomputational inverse problem

Mathematics Subject Classification ID

Ill-posedness and regularization problems in numerical linear algebra (65F22) 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)

Related Items (3)

Neural networks for classification of strokes in electrical impedance tomography on a 3D head model ⋮ Joint reconstruction in low dose multi-energy CT ⋮ State estimation with structural priors in fMRI

Cites Work

• Unnamed Item
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• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Anisotropic total variation filtering
• A first-order primal-dual algorithm for convex problems with applications to imaging
• Exponential instability in an inverse problem for the Schrödinger equation
• Spatially dependent regularization parameter selection in total generalized variation models for image restoration
• A Variational Approach for Sharpening High Dimensional Images
• A D-Bar Algorithm with A Priori Information for 2-Dimensional Electrical Impedance Tomography
• Multicontrast MRI Reconstruction with Structure-Guided Total Variation
• Solution-Driven Adaptive Total Variation Regularization
• Existence and Uniqueness for Electrode Models for Electric Current Computed Tomography
• Inverse problems with structural prior information
• Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography
• Dynamic MRI reconstruction from undersampled data with an anatomical prescan
• Adaptive Regularization With the Structure Tensor
• Blind image fusion for hyperspectral imaging with the directional total variation
• Stable determination of conductivity by boundary measurements
• Computational Methods for Inverse Problems

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