EIDORS

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 35 articles )

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  1. Bar, Leah; Sochen, Nir: Strong solutions for PDE-based tomography by unsupervised learning (2021)
  2. Iglesias, Marco; Yang, Yuchen: Adaptive regularisation for ensemble Kalman inversion (2021)
  3. Karimi, Ahmad; Taghizadeh, Leila; Heitzinger, Clemens: Optimal Bayesian experimental design for electrical impedance tomography in medical imaging (2021)
  4. Wang, Jing: Non-convex (\ell_p) regularization for sparse reconstruction of electrical impedance tomography (2021)
  5. Wang, Jing; Han, Bo: Application of a class of iterative algorithms and their accelerations to Jacobian-based linearized EIT image reconstruction (2021)
  6. Watson, F. M.; Crabb, M. G.; Lionheart, W. R. B.: A polarization tensor approximation for the Hessian in iterative solvers for non-linear inverse problems (2021)
  7. Huska, M.; Lazzaro, D.; Morigi, Serena; Samorè, A.; Scrivanti, G.: Spatially-adaptive variational reconstructions for linear inverse electrical impedance tomography (2020)
  8. 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)
  9. Thomas Dowrick, James Avery, Mayo Faulkner, David Holder, Kirill Aristovich: EIT-MESHER - Segmented FEM Mesh Generation and Refinement (2020) not zbMATH
  10. Yazdanian, Hassan; Saturnino, Guilherme B.; Thielscher, Axel; Knudsen, Kim: Fast evaluation of the Biot-Savart integral using FFT for electrical conductivity imaging (2020)
  11. Calvetti, D.; Nakkireddy, S.; Somersalo, Erkki: Approximation of continuous EIT data from electrode measurements with Bayesian methods (2019)
  12. Oates, Chris J.; Cockayne, Jon; Aykroyd, Robert G.; Girolami, Mark: Bayesian probabilistic numerical methods in time-dependent state estimation for industrial hydrocyclone equipment (2019)
  13. Wang, Jing; Han, Bo; Wang, Wei: Elastic-net regularization for nonlinear electrical impedance tomography with a splitting approach (2019)
  14. 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
  15. Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.: Parameterizations for ensemble Kalman inversion (2018)
  16. Harrach, Bastian; Minh, Mach Nguyet: Monotonicity-based regularization for phantom experiment data in electrical impedance tomography (2018)
  17. Hetrick, Hank; Mead, Jodi: Geophysical imaging of subsurface structures with least squares estimates (2018)
  18. Ren, Shangjie; Soleimani, Manuchehr; Xu, Yaoyuan; Dong, Feng: Inclusion boundary reconstruction and sensitivity analysis in electrical impedance tomography (2018)
  19. Crabb, M. G.: Convergence study of (2D) forward problem of electrical impedance tomography with high-order finite elements (2017)
  20. Dunlop, Matthew M.; Iglesias, Marco A.; Stuart, Andrew M.: Hierarchical Bayesian level set inversion (2017)

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