EIDORS is a MATLAB based software library that aims to provide free software algorithms for forward modelling and inverse solutions of Electrical Impedance and (to some extent) Diffusion-based Optical Tomography, in medical, industrial and geophysical settings and to share data and promote collaboration. Release 3.8 of EIDORS builds upon a strong foundation in reconstruction algorithms, adding and improving a number of aspects.

References in zbMATH (referenced in 33 articles )

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  1. Iglesias, Marco; Yang, Yuchen: Adaptive regularisation for ensemble Kalman inversion (2021)
  2. Karimi, Ahmad; Taghizadeh, Leila; Heitzinger, Clemens: Optimal Bayesian experimental design for electrical impedance tomography in medical imaging (2021)
  3. Wang, Jing: Non-convex (\ell_p) regularization for sparse reconstruction of electrical impedance tomography (2021)
  4. Wang, Jing; Han, Bo: Application of a class of iterative algorithms and their accelerations to Jacobian-based linearized EIT image reconstruction (2021)
  5. Huska, M.; Lazzaro, D.; Morigi, Serena; Samorè, A.; Scrivanti, G.: Spatially-adaptive variational reconstructions for linear inverse electrical impedance tomography (2020)
  6. Taghizadeh, Leila; Karimi, Ahmad; Stadlbauer, Benjamin; Weninger, Wolfgang J.; Kaniusas, Eugenijus; Heitzinger, Clemens: Bayesian inversion for electrical-impedance tomography in medical imaging using the nonlinear Poisson-Boltzmann equation (2020)
  7. Thomas Dowrick, James Avery, Mayo Faulkner, David Holder, Kirill Aristovich: EIT-MESHER - Segmented FEM Mesh Generation and Refinement (2020) not zbMATH
  8. Yazdanian, Hassan; Saturnino, Guilherme B.; Thielscher, Axel; Knudsen, Kim: Fast evaluation of the Biot-Savart integral using FFT for electrical conductivity imaging (2020)
  9. Calvetti, D.; Nakkireddy, S.; Somersalo, Erkki: Approximation of continuous EIT data from electrode measurements with Bayesian methods (2019)
  10. Oates, Chris J.; Cockayne, Jon; Aykroyd, Robert G.; Girolami, Mark: Bayesian probabilistic numerical methods in time-dependent state estimation for industrial hydrocyclone equipment (2019)
  11. Wang, Jing; Han, Bo; Wang, Wei: Elastic-net regularization for nonlinear electrical impedance tomography with a splitting approach (2019)
  12. Benyuan Liu; Bin Yang; Canhua Xu; Junying Xia; Meng Dai; Zhenyu Ji; Fusheng You; Xiuzhen Dong; Xuetao Shi; Feng Fu: pyEIT: A python based framework for Electrical Impedance Tomography (2018) not zbMATH
  13. Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.: Parameterizations for ensemble Kalman inversion (2018)
  14. Harrach, Bastian; Minh, Mach Nguyet: Monotonicity-based regularization for phantom experiment data in electrical impedance tomography (2018)
  15. Hetrick, Hank; Mead, Jodi: Geophysical imaging of subsurface structures with least squares estimates (2018)
  16. Ren, Shangjie; Soleimani, Manuchehr; Xu, Yaoyuan; Dong, Feng: Inclusion boundary reconstruction and sensitivity analysis in electrical impedance tomography (2018)
  17. Crabb, M. G.: Convergence study of (2D) forward problem of electrical impedance tomography with high-order finite elements (2017)
  18. Dunlop, Matthew M.; Iglesias, Marco A.; Stuart, Andrew M.: Hierarchical Bayesian level set inversion (2017)
  19. Suryanto, B.; Saraireh, D.; Kim, J.; McCarter, W. J.; Starrs, G.; Taha, H. M.: Imaging water ingress into concrete using electrical resistance tomography (2017)
  20. Aykroyd, Robert G.; Barber, Stuart; Miller, Luke R.: Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring (2016)

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