We state the Limited Electrode problem in Electrical Impedance Tomography, and propose solutions inspired by the application of compressed sensing techniques and deep learning strategies on the raw boundary impedance data. These strategies allow to recover the target reconstruction quality while using a relatively low number of nonlinear measurements, assuming sparsity-gradient conductivity. This would help reducing modelling costs and computational power, thus enhancing applicability of EIT.
Limited Electrodes Models in Electrical Impedance Tomography Reconstruction
Colibazzi F.;Lazzaro D.;Morigi S.
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2023
Abstract
We state the Limited Electrode problem in Electrical Impedance Tomography, and propose solutions inspired by the application of compressed sensing techniques and deep learning strategies on the raw boundary impedance data. These strategies allow to recover the target reconstruction quality while using a relatively low number of nonlinear measurements, assuming sparsity-gradient conductivity. This would help reducing modelling costs and computational power, thus enhancing applicability of EIT.File in questo prodotto:
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