Electrical Impedance Tomography (EIT) is a well-known imaging technique for detecting the electrical properties of an object in order to detect anomalies, such as conductive or resistive targets. More specifically, EIT has many applications in medical imaging for the detection and location of bodily tumors since it is an affordable and non-invasive method, which aims to recover the internal conductivity of a body using voltage measurements resulting from applying low frequency current at electrodes placed at its surface. Mathematically, the reconstruction of the internal conductivity is a severely ill-posed inverse problem and yields a poor quality image reconstruction. To remedy this difficulty, at least in part, we regularize and solve the nonlinear minimization problem by the aid of a Krylov subspace-type method for the linear sub problem during each iteration. In EIT, a tumor or general anomaly can be modeled as a piecewise constant perturbation of a smooth background, hence, we solve the regularized problem on a subspace of relatively small dimension by the Flexible Golub-Kahan process that provides solutions that have sparse representation. For comparison, we use a well-known modified Gauss–Newton algorithm as a benchmark. Using simulations, we demonstrate the effectiveness of the proposed method. The obtained reconstructions indicate that the Krylov subspace method is better adapted to solve the ill-posed EIT problem and results in higher resolution images and faster convergence compared to reconstructions using the modified Gauss–Newton algorithm.
Keywords: EIT, regularization, Golub-Kahan, sparsity, Krylov subspace methods
@article{M2AN_2021__55_6_2827_0,
author = {Pasha, Mirjeta and Kupis, Shyla and Ahmad, Sanwar and Khan, Taufiquar},
title = {A {Krylov} subspace type method for {Electrical} {Impedance} {Tomography}},
journal = {ESAIM: Mathematical Modelling and Numerical Analysis },
pages = {2827--2847},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {6},
doi = {10.1051/m2an/2021057},
mrnumber = {4344861},
language = {en},
url = {https://www.numdam.org/articles/10.1051/m2an/2021057/}
}
TY - JOUR AU - Pasha, Mirjeta AU - Kupis, Shyla AU - Ahmad, Sanwar AU - Khan, Taufiquar TI - A Krylov subspace type method for Electrical Impedance Tomography JO - ESAIM: Mathematical Modelling and Numerical Analysis PY - 2021 SP - 2827 EP - 2847 VL - 55 IS - 6 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/m2an/2021057/ DO - 10.1051/m2an/2021057 LA - en ID - M2AN_2021__55_6_2827_0 ER -
%0 Journal Article %A Pasha, Mirjeta %A Kupis, Shyla %A Ahmad, Sanwar %A Khan, Taufiquar %T A Krylov subspace type method for Electrical Impedance Tomography %J ESAIM: Mathematical Modelling and Numerical Analysis %D 2021 %P 2827-2847 %V 55 %N 6 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/m2an/2021057/ %R 10.1051/m2an/2021057 %G en %F M2AN_2021__55_6_2827_0
Pasha, Mirjeta; Kupis, Shyla; Ahmad, Sanwar; Khan, Taufiquar. A Krylov subspace type method for Electrical Impedance Tomography. ESAIM: Mathematical Modelling and Numerical Analysis , Tome 55 (2021) no. 6, pp. 2827-2847. doi: 10.1051/m2an/2021057
[1] , , , , and , Efficient parallel iterative solvers for the solution of large dense linear systems arising from the boundary element method in electromagnetism. In: Proceedings of the International Conference on Supercomputing in Nuclear Application (SNA), Paris (2003).
[2] , The problem of the convergence of the iteratively regularized Gauss-Newton method. Comput. Math. Math. Phys. 32 (1992) 1353–1359. | MR | Zbl
[3] , Iterative methods for solving nonlinear operator equations without regularity. A new approach. Doklady Akademii Nauk 330 (1993) 282–284. | Zbl | MR
[4] and , On application of generalized discrepancy principle to iterative methods for nonlinear ill-posed problems. Numer. Funct. Anal. Optim. 26 (2005) 35–48. | MR | Zbl | DOI
[5] , MCMC-based image reconstruction with uncertainty quantification. SIAM J. Sci. Comput. 34 (2012) A1316–A1332. | MR | Zbl | DOI
[6] , , , , , , , , and , Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods. SIAM (1994). | MR | Zbl
[7] , Bioimpedance tomography (electrical impedance tomography). Annu. Rev. Biomed. Eng. 8 (2006) 63–91. | DOI
[8] and , Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans. Image Process. 18 (2009) 2419–2434. | MR | DOI
[9] , Preconditioning techniques for large linear systems: A survey. J. Comput. Phys. 182 (2002) 418–477. | MR | Zbl | DOI
[10] , and , On convergence rates for the iteratively regularized Gauss-Newton method. IMA J. Numer. Anal. 17 (1997) 421–436. | MR | Zbl | DOI
[11] and , A nested Krylov subspace method for the overlap operator. Preprint (2009). | arXiv
[12] , Electrical impedance tomography. Inverse Prob. 18 (2002) R99. | MR | Zbl | DOI
[13] , , , and , Imaging heterogeneities with electrical impedance tomography: laboratory results. Géotechnique 55 (2005) 539–547. | DOI
[14] , A Survey of Preconditioned Iterative Methods. CRC Press 328 (1995). | MR | Zbl
[15] , and , Modulus-based iterative methods for constrained minimization. Inverse Prob. 36 (2020) 084001. | MR | DOI
[16] , and , Generalized singular value decomposition with iterated Tikhonov regularization. J. Comput. Appl. Math. 373 (2020) 112276. | MR | DOI
[17] , and , Linearized Krylov subspace Bregman iteration with nonnegativity constraint. Numer. Algorithms 87 (2021) 1177–1200. | MR | DOI
[18] , Matrix Preconditioning Techniques and Applications. Cambridge University Press 19 (2005). | MR | Zbl | DOI
[19] , and , Electrical impedance tomography. SIAM Rev. 41 (1999) 85–101. | MR | Zbl | DOI
[20] , , and , Electrode models for electric current computed tomography. IEEE Trans. Biomed. Eng. 36 (1989) 918–924. | DOI
[21] and , Flexible Krylov methods for regularization. SIAM J. Sci. Comput. 41 (2019) S149–S171. | MR | DOI
[22] and , Computational methods for large-scale inverse problems: A survey on hybrid projection methods. Preprint (2021). | arXiv | MR
[23] , , , and , 3D-electrical resistivity tomography monitoring of salt transport in homogeneous and layered soil samples. Acta Geotech. 6 (2011) 195–203. | DOI
[24] , , and , Estimation of the hydraulic parameters of unsaturated samples by electrical resistivity tomography. Géotechnique 62 (2012) 583–594. | DOI
[25] , , and , Electrical resistivity tomography of vadose water movement. Water Res. Res. 28 (1992) 1429–1442. | DOI
[26] , Truncation strategies for optimal Krylov subspace methods. SIAM J. Numer. Anal. 36 (1999) 864–889. | MR | Zbl | DOI
[27] and , Parallel multigrid methods for the calculation of unsteady flows on unstructured grids: algorithmic aspects and parallel performances on clusters of PCS. Parallel Comput. 30 (2004) 503–525. | MR | DOI
[28] , Denoising by soft-thresholding. IEEE Trans. Inf. Theory 41 (1995) 613–627. | MR | Zbl | DOI
[29] , , and , Numerical stability of GMRES. BIT Numer. Math. 35 (1995) 309–330. | MR | Zbl | DOI
[30] , and , A multigrid method enhanced by Krylov subspace iteration for discrete Helmholtz equations. SIAM J. Sci. Comput. 23 (2001) 1291–1315. | MR | Zbl | DOI
[31] and , Generalized Arnoldi-Tikhonov method for sparse reconstruction. SIAM J. Sci. Comput. 36 (2014) B225–B247. | MR | Zbl | DOI
[32] , and , Iteratively reweighted FGMRES and FLSQR for sparse reconstruction. SIAM J. Sci. Comput. (2021) S47–S69. | MR | DOI
[33] , and , A comparison of preconditioned Krylov subspace methods for large-scale nonsymmetric linear systems. Numer. Linear Algebra App. 26 (2019) e2215. | MR | DOI
[34] and , The split bregman method for -regularized problems. SIAM J. Imaging Sci. 2 (2009) 323–343. | MR | Zbl | DOI
[35] and , Matrix Computations. Johns Hopkins University Press (1989). | MR | Zbl
[36] and , Matrix Computations, 4th edition. Johns Hopkins University Press, Baltimore (2013). | MR | Zbl | DOI
[37] and , Inexact preconditioned conjugate gradient method with inner-outer iteration. SIAM J. Sci. Comput. 21 (1999) 1305–1320. | MR | Zbl | DOI
[38] and , Inexact inverse iteration for generalized eigenvalue problems. BIT Numer. Math. 40 (2000) 671–684. | MR | Zbl | DOI
[39] and , Recent progress in electrical impedance tomography. Inverse Prob. 19 (2003) S65. | MR | Zbl | DOI
[40] , Discrete Inverse Problems: Insight and Algorithms. SIAM (2010). | MR | Zbl | DOI
[41] , and , The regularizing effect of the Golub-Kahan iterative bidiagonalization and revealing the noise level in the data. BIT Numer. Math. 49 (2009) 669–696. | MR | Zbl | DOI
[42] , Logarithmic convergence rates of the iteratively regularized Gauss-Newton method for an inverse potential and an inverse scattering problem. Inverse Prob. 13 (1997) 1279–1299. | MR | Zbl | DOI
[43] , , , and , Majorization-minimization generalized Krylov subspace methods for optimization applied to image restoration. BIT Numer. Math. 57 (2017) 351–378. | MR | DOI
[44] , A note on preconditioning nonsymmetric matrices. SIAM J. Sci. Comput. 23 (2001) 1050–1051. | MR | Zbl | DOI
[45] , , and , Reconstructions of chest phantoms by the D-bar method for electrical impedance tomography. IEEE Trans. Med. Imaging 23 (2004) 821–828. | DOI
[46] , and , A capacitance-based tomography system for interface measurement in separation vessels. Meas. Sci. Technol. 5 (1994) 1262. | DOI
[47] and , An analysis of electrical impedance tomography with applications to Tikhonov regularization. ESAIM: Control Optim. Calculus Variations 18 (2012) 1027–1048. | MR | Zbl | Numdam
[48] and , Sparsity regularization for parameter identification problems. Inverse Prob. 28 (2012) 123001. | MR | Zbl | DOI
[49] , and , A reconstruction algorithm for electrical impedance tomography based on sparsity regularization. Int. J. Numer. Methods Eng. 89 (2012) 337–353. | MR | Zbl | DOI
[50] , , and , Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography. Inverse Prob. 16 (2000) 1487–1522. | MR | Zbl | DOI
[51] , , and , Posterior covariance related optimal current patterns in electrical impedance tomography. Inverse Prob. 20 (2004) 919. | MR | Zbl | DOI
[52] and , The Factorization Method for Inverse Problems. Oxford University Press 36 (2008). | MR | Zbl
[53] , , and , A generalized Krylov subspace method for minimization. SIAM J. Sci. Comput. 37 (2015) S30–S50. | MR | Zbl | DOI
[54] and , Newton regularizations for impedance tomography: A numerical study. Inverse Prob. 22 (2006) 1967. | MR | Zbl | DOI
[55] and , Arnoldi-Tikhonov regularization methods. J. Comput. Appl. Math. 226 (2009) 92–102. | MR | Zbl | DOI
[56] , and , Least squares residuals and minimal residual methods. SIAM J. Sci. Comput. 23 (2002) 1503–1525. | MR | Zbl | DOI
[57] , and , The Bayesian approximation error approach for electrical impedance tomography-experimental results. Meas. Sci. Technol. 19 (2007) 015501. | DOI
[58] , , and , Compensation of errors due to discretization, domain truncation and unknown contact impedances in electrical impedance tomography. Meas. Sci. Technol. 20 (2009) 105504. | DOI
[59] and , Numerical Optimization. Springer (2006). | MR | Zbl
[60] , Flexible conjugate gradients. SIAM J. Sci. Comput. 22 (2000) 1444–1460. | MR | Zbl | DOI
[61] , and , Modified Gram-Schmidt (MGS), least squares, and backward stability of MGS-GMRES. SIAM J. Matrix Anal. App. 28 (2006) 264–284. | MR | Zbl | DOI
[62] , Electrical impedance tomography: methods, history, and applications (institute of physics medical physics series). Phys. Med. Biol. 50 (2005) 2427. | DOI
[63] and , An efficient algorithm for sparse representations with l$$ data fidelity term. In: Proceedings of 4th IEEE Andean Technical Conference (ANDESCON) (2008).
[64] , A flexible inner-outer preconditioned GMRES algorithm. SIAM J. Sci. Comput. 14 (1993) 461–469. | MR | Zbl | DOI
[65] , Iterative Methods for Sparse Linear Systems. SIAM (2003). | MR | Zbl | DOI
[66] and , GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems. SIAM J. Sci. Stat. Comput. 7 (1986) 856–869. | MR | Zbl | DOI
[67] and , Flexible inner-outer Krylov subspace methods. SIAM J. Numer. Anal. 40 (2002) 2219–2239. | MR | Zbl | DOI
[68] and , Recent computational developments in Krylov subspace methods for linear systems. Numer. Linear Algebra App. 14 (2007) 1–59. | MR | Zbl | DOI
[69] , and , Differences in the effects of rounding errors in Krylov solvers for symmetric indefinite linear systems. SIAM J. Matrix Anal. App. 22 (2001) 726–751. | MR | Zbl | DOI
[70] , and , Convergence and application of a modified iteratively regularized Gauss-Newton algorithm. Inverse Prob. 23 (2007) 1547–1563. | MR | Zbl | DOI
[71] , and , Existence and uniqueness for electrode models for electric current computed tomography. SIAM J. Appl. Math. 52 (1992) 1023–1040. | MR | Zbl | DOI
[72] and , FQMR: A flexible quasi-minimal residual method with inexact preconditioning. SIAM J. Sci. Comput. 23 (2001) 363–380. | MR | Zbl | DOI
[73] , An Introduction to Data Analysis and Uncertainty Quantification for Inverse Problems. SIAM (2017). | MR
[74] and , Inexact Krylov subspace methods for linear systems. SIAM J. Matrix Anal. App. 26 (2004) 125–153. | MR | Zbl | DOI
[75] and , GMRESR: a family of nested GMRES methods. Numer. Linear Algebra App. 1 (1994) 369–386. | MR | Zbl | DOI
[76] and , Matrix Computations, Johns Hopkins University Press Baltimore (1983). | MR | Zbl
[77] , , and , Iterative image reconstruction in three-dimensional electrical impedance tomography. Inverse Prob. Design Optim. 1 (2004) 152.
[78] , Flexible BICG and flexible BI-CGSTAB for nonsymmetric linear systems. Appl. Math. Comput. 188 (2007) 226–233. | MR | Zbl
[79] , New insights in GMRES-like methods with variable preconditioners. J. Comput. Appl. Math. 61 (1995) 189–204. | MR | Zbl | DOI
[80] and , A comparison of some GMRES-like methods. Linear Algebra App. 160 (1992) 131–162. | MR | Zbl | DOI
[81] and , Photoacoustic and thermoacoustic tomography: image formation principles. In: Handbook of Mathematical Methods in Imaging (2015). | MR | Zbl | DOI
[82] , , and , Preconditioning a mixed discontinuous finite element method for radiation diffusion. Numer. Linear Algebra App. 11 (2004) 795–811. | MR | Zbl | DOI
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