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PubMed · 15889545

Improving the forward solver for the complete electrode model in EIT using algebraic multigrid.

Manuchehr Soleimani·Catherine E Powell·Nick Polydorides
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Abstract

Image reconstruction in electrical impedance tomography is an ill-posed nonlinear inverse problem. Linearization techniques are widely used and require the repeated solution of a linear forward problem. To account correctly for the presence of electrodes and contact impedances, the so-called complete electrode model is applied. Implementing a standard finite element method for this particular forward problem yields a linear system that is symmetric and positive definite and solvable via the conjugate gradient method. However, preconditioners are essential for efficient convergence. Preconditioners based on incomplete factorization methods are commonly used but their performance depends on user-tuned parameters. To avoid this deficiency, we apply black-box algebraic multigrid, using standard commercial and freely available software. The suggested solution scheme dramatically reduces the time cost of solving the forward problem. Numerical results are presented using an anatomically detailed model of the human head.

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AlgorithmsArtificial IntelligenceBrainComputer SimulationElectric ImpedanceElectrodesHumansImage EnhancementImage Interpretation, Computer-AssistedImaging, Three-DimensionalModels, BiologicalPhantoms, ImagingPlethysmography, ImpedanceTomographyEvaluation StudyJournal ArticleValidation Study

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Manuchehr Soleimani, Catherine E Powell, Nick Polydorides. 2005. Improving the forward solver for the complete electrode model in EIT using algebraic multigrid.. https://doi.org/10.1109/tmi.2005.843741

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