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 31 articles )

Showing results 1 to 20 of 31.
Sorted by year (citations)

1 2 next

  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. Huska, M.; Lazzaro, D.; Morigi, Serena; Samorè, A.; Scrivanti, G.: Spatially-adaptive variational reconstructions for linear inverse electrical impedance tomography (2020)
  4. 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)
  5. Thomas Dowrick, James Avery, Mayo Faulkner, David Holder, Kirill Aristovich: EIT-MESHER - Segmented FEM Mesh Generation and Refinement (2020) not zbMATH
  6. Yazdanian, Hassan; Saturnino, Guilherme B.; Thielscher, Axel; Knudsen, Kim: Fast evaluation of the Biot-Savart integral using FFT for electrical conductivity imaging (2020)
  7. Calvetti, D.; Nakkireddy, S.; Somersalo, Erkki: Approximation of continuous EIT data from electrode measurements with Bayesian methods (2019)
  8. Oates, Chris J.; Cockayne, Jon; Aykroyd, Robert G.; Girolami, Mark: Bayesian probabilistic numerical methods in time-dependent state estimation for industrial hydrocyclone equipment (2019)
  9. Wang, Jing; Han, Bo; Wang, Wei: Elastic-net regularization for nonlinear electrical impedance tomography with a splitting approach (2019)
  10. 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
  11. Chada, Neil K.; Iglesias, Marco A.; Roininen, Lassi; Stuart, Andrew M.: Parameterizations for ensemble Kalman inversion (2018)
  12. Harrach, Bastian; Minh, Mach Nguyet: Monotonicity-based regularization for phantom experiment data in electrical impedance tomography (2018)
  13. Hetrick, Hank; Mead, Jodi: Geophysical imaging of subsurface structures with least squares estimates (2018)
  14. Ren, Shangjie; Soleimani, Manuchehr; Xu, Yaoyuan; Dong, Feng: Inclusion boundary reconstruction and sensitivity analysis in electrical impedance tomography (2018)
  15. Crabb, M. G.: Convergence study of (2D) forward problem of electrical impedance tomography with high-order finite elements (2017)
  16. Dunlop, Matthew M.; Iglesias, Marco A.; Stuart, Andrew M.: Hierarchical Bayesian level set inversion (2017)
  17. Suryanto, B.; Saraireh, D.; Kim, J.; McCarter, W. J.; Starrs, G.; Taha, H. M.: Imaging water ingress into concrete using electrical resistance tomography (2017)
  18. Aykroyd, Robert G.; Barber, Stuart; Miller, Luke R.: Classification of multiple time signals using localized frequency characteristics applied to industrial process monitoring (2016)
  19. Dunlop, Matthew M.; Stuart, Andrew M.: The Bayesian formulation of EIT: analysis and algorithms (2016)
  20. Iglesias, Marco A.: A regularizing iterative ensemble Kalman method for PDE-constrained inverse problems (2016)

1 2 next