American Institute of Mathematical Sciences

Advanced
  • Previous Article
    An augmented lagrangian method for solving a new variational model based on gradients similarity measures and high order regulariation for multimodality registration
  • IPI Home
  • This Issue
  • Next Article
    On finding the surface admittance of an obstacle via the time domain enclosure method
April  2019, 13(2): 285-307. doi: 10.3934/ipi.2019015

Incorporating structural prior information and sparsity into EIT using parallel level sets

Ville Kolehmainen 1,, , Matthias J. Ehrhardt 2, and  Simon R. Arridge 3,

1. 

Department of Applied Physics, University of Eastern Finland, POB 1627, FI-70211 Kuopio, Finland
2. 

Institute for Mathematical Innovation, University of Bath, Bath BA2 7AY, UK
3. 

Centre for Medical Image Computing, University College London, Gower Street, London, WC1E 6BT, UK

* Corresponding author: Ville Kolehmainen

Received  September 2017 Revised  June 2018 Published  January 2019

EIT is a non-linear ill-posed inverse problem which requires sophisticated regularisation techniques to achieve good results. In this paper we consider the use of structural information in the form of edge directions coming from an auxiliary image of the same object being reconstructed. In order to allow for cases where the auxiliary image does not provide complete information we consider in addition a sparsity regularization for the edges appearing in the EIT image. The combination of these approaches is conveniently described through the parallel level sets approach. We present an overview of previous methods for structural regularisation and then provide a variational setting for our approach and explain the numerical implementation. We present results on simulations and experimental data for different cases with accurate and inaccurate prior information. The results demonstrate that the structural prior information improves the reconstruction accuracy, even in cases when there is reasonable uncertainty in the prior about the location of the edges or only partial edge information is available.

Keywords: Electrical impedance tomography, structural prior, computational inverse problem, regularization, finite element method.
Mathematics Subject Classification: Primary: 65N21, 65F22; Secondary: 65N30.
Citation: Ville Kolehmainen, Matthias J. Ehrhardt, Simon R. Arridge. Incorporating structural prior information and sparsity into EIT using parallel level sets. Inverse Problems & Imaging, 2019, 13 (2) : 285-307. doi: 10.3934/ipi.2019015
References:
[1]

G. Alessandrini, Stable determination of conductivity by boundary measurements, Applicable Analysis, 27 (1988), 153-172.  doi: 10.1080/00036818808839730.  Google Scholar

[2]

M. Alsaker and J. Mueller, A d-bar algorithm with a priori information for 2-dimensional electrical impedance tomography, SIAM Journal on Imaging Sciences, 9 (2016), 1619-1654.  doi: 10.1137/15M1020137.  Google Scholar

[3]

S. R. Arridge, V. Kolehmainen and M. J. Schweiger, Reconstruction and Regularisation in Optical Tomography, in Interdisciplinary Workshop on Mathematical Methods in Biomedical Imaging and Intensity-Modulated Radiation, 2007. Google Scholar

[4]

B. Bai, Q. Li and R. M. Leahy, Magnetic Resonance-guided Positron Emission Tomography Image Reconstruction, Seminars in Nuclear Medicine, 43 (2013), 30-44.  doi: 10.1053/j.semnuclmed.2012.08.006.  Google Scholar

[5]

C. Ballester, V. Caselles, L. Igual, J. Verdera and B. Rougé, A Variational Model for P+XS Image Fusion, International Journal of Computer Vision, 69 (2006), 43-58.  doi: 10.1007/s11263-006-6852-x.  Google Scholar

[6]

C. Bathke, T. Kluth, C. Brandt and P. Maa, Improved image reconstruction in magnetic particle imaging using structural a priori information, International Journal on Magnetic Particle Imaging, 3, URL https://journal.iwmpi.org/index.php/iwmpi/article/view/64. Google Scholar

[7]

I. Bayram and M. E. Kamasak, A Directional Total Variation, IEEE Signal Processing Letters, 19 (2012), 781-784.  doi: 10.1109/LSP.2012.2220349.  Google Scholar

[8]

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, 2 (2009), 183-202.  doi: 10.1137/080716542.  Google Scholar

[9]

A. Björck, Numerical Methods for Least Squares Problems, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1996. doi: 10.1137/1.9781611971484.  Google Scholar

[10]

J. E. Bowsher, H. Yuan, L. W. Hedlund, T. G. Turkington, G. Akabani, A. Badea, W. C. Kurylo, C. T. Wheeler, G. P. Cofer, M. W. Dewhirst and G. A. Johnson, Utilizing MRI Information to Estimate F18-FDG Distributions in Rat Flank Tumors, in IEEE Nuclear Science Symposium and Medical Imaging Conference, 2004, 2488–2492, http://ieeexplore.ieee.org/xpl/login.jsp?arnumber=1462760 & http://ieeexplore.ieee.org/xpls/abs{_}all.jsp?arnumber=1462760. doi: 10.1109/NSSMIC.2004.1462760.  Google Scholar

[11]

K. Bredies, Y. Dong and M. Hintermüller, Spatially dependent regularization parameter selection in total generalized variation models for image restoration, International Journal of Computer Mathematics, 90 (2013), 109-123.  doi: 10.1080/00207160.2012.700400.  Google Scholar

[12]

L. Bungert, D. A. Coomes, M. J. Ehrhardt, J. Rasch, R. Reisenhofer and C.-B. Schönlieb, Blind image fusion for hyperspectral imaging with the directional total variation, Inverse Problems, 34 (2008), 044003, 23 pp, URL http://arXiv.org/abs/1710.05705. doi: 10.1088/1361-6420/aaaf63.  Google Scholar

[13]

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145.  doi: 10.1007/s10851-010-0251-1.  Google Scholar

[14]

K.-S. Cheng, D. Isaacson, J. Newell and D. G. Gisser, Electrode models for electric current computed tomography, IEEE Transactions Biomed. Eng., 36 (1989), 918–924. Google Scholar

[15]

S. R. Cherry, Multimodality in vivo imaging systems: twice the power or double the trouble?, Annual Review of Biomedical Engineering, 8 (2006), 35-62.  doi: 10.1146/annurev.bioeng.8.061505.095728.  Google Scholar

[16]

T. Dowrick, C. Blochet and D. Holder, In vivo bioimpedance measurement of healthy and ischaemic rat brain: implications for stroke imaging using electrical impedance tomography, Physiological Measurement, 36 (2015), 1273, http://stacks.iop.org/0967-3334/36/i=6/a=1273. Google Scholar

[17]

M. J. Ehrhardt and M. M. Betcke, Multi-contrast MRI reconstruction with structure-guided total variation, SIAM Journal on Imaging Sciences, 9 (2016), 1084-1106.  doi: 10.1137/15M1047325.  Google Scholar

[18]

M. J. Ehrhardt, P. Markiewicz, M. Liljeroth, A. Barnes, V. Kolehmainen, J. Duncan, L. Pizarro, D. Atkinson, B. F. Hutton, S. Ourselin, K. Thielemans and S. R. Arridge, PET reconstruction with an anatomical MRI Prior using parallel level sets, IEEE Transactions on Medical Imaging, 35 (2016), 2189-2199, URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7452643. doi: 10.1109/TMI.2016.2549601.  Google Scholar

[19]

V. Estellers, S. Soatto and X. Bresson, Adaptive Regularization With the Structure Tensor, IEEE Transactions on Image Processing, 24 (2015), 1777-1790.  doi: 10.1109/TIP.2015.2409562.  Google Scholar

[20]

F. Fang, F. Li, C. Shen and G. Zhang, A Variational Approach for Pan-Sharpening, IEEE Transactions on Image Processing, 22 (2013), 2822-2834.  doi: 10.1109/TIP.2013.2258355.  Google Scholar

[21]

I. Frerichs, M. B. P. Amato, A. H. van Kaam, D. G. Tingay, Z. Zhao, B. Grychtol, M. Bodenstein, H. Gagnon, S. H. Bohm, E. Teschner, O. Stenqvist, T. Mauri, V. Torsani, L. Camporota, A. Schibler, G. K. Wolf, D. Gommers, S. Leonhardt and A. Adler, Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the translational eit development study group, Thorax, 72 (2017), 83-93, URL http://thorax.bmj.com/content/72/1/83. doi: 10.1136/thoraxjnl-2016-208357.  Google Scholar

[22]

M. Grasmair, Locally adaptive total variation regularization, in SSVM, LNCS, 5567 (2009), 331–342. doi: 10.1007/978-3-642-02256-2_28.  Google Scholar

[23]

M. Grasmair and F. Lenzen, Anisotropic total variation filtering, Optimization, 62 (2010), 323-339.  doi: 10.1007/s00245-010-9105-x.  Google Scholar

[24]

L. M. Heikkinen, T. Vilhunen, R. M. West and M. Vauhkonen, Simultaneous reconstruction of electrode contact impedances and internal electrical properties: Ii. laboratory experiments, Meas. Sci. Technol., 13 (2002), 1855-1861.  doi: 10.1088/0957-0233/13/12/308.  Google Scholar

[25]

B. F. Hutton, B. A. Thomas, K. Erlandsson, A. Bousse, A. Reilhac-Laborde, D. Kazantsev, S. Pedemonte, K. Vunckx, S. R. Arridge and S. Ourselin, What Approach to Brain Partial Volume Correction is best for PET/MRI?, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 702 (2013), 29-33.  doi: 10.1016/j.nima.2012.07.059.  Google Scholar

[26]

J. P. Kaipio, V. Kolehmainen, E. Somersalo and M. Vauhkonen, Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography, Inv Probl, 16 (2000), 1487-1522.  doi: 10.1088/0266-5611/16/5/321.  Google Scholar

[27]

J. P. Kaipio, V. Kolehmainen, M. Vauhkonen and E. Somersalo, Inverse problems with structural prior information, Inverse Problems, 15 (1999), 713-729.  doi: 10.1088/0266-5611/15/3/306.  Google Scholar

[28]

D. Kazantsev, W. R. B. Lionheart, P. J. Withers and P. D. Lee, Multimodal Image Reconstruction using Supplementary Structural Information in Total Variation Regularization, Sensing and Imaging, 15 (2014), 97. doi: 10.1007/s11220-014-0097-5.  Google Scholar

[29]

F. Knoll, Y. Dong, C. Langskammer, M. Hintermüller and R. Stollberger, Total variation denoising with spatially dependent regularization, in Proc. Intl. Soc. Mag. Reson. Med., 18 (2010), 5088. Google Scholar

[30]

V. Kolehmainen, M. Lassas and P. Ola, Electrical impedance tomography problem with inaccurately known boundary and contact impedances, IEEE Transactions on Medical Imaging, 27 (2008), 1404-1414.  doi: 10.1109/ISBI.2006.1625120.  Google Scholar

[31]

J. Kourunen, T. Savolainen, A. Lehikoinen, M. Vauhkonen and L. M. Heikkinen, Suitability of a pxi platform for an electrical impedance tomography system, Meas. Sci. Technol., 20 (2009), 015503. doi: 10.1088/0957-0233/20/1/015503.  Google Scholar

[32]

R. M. Leahy and X. Yan, Incorporation of Anatomical MR Data for Improved Functional Imaging with PET, in Information Processing in Medical Imaging, Springer, 1991,105–120. doi: 10.1007/BFb0033746.  Google Scholar

[33]

F. Lenzen and J. Berger, Solution-driven adaptive total variation regularization, in SSVM, 9087 (2015), 203-215, URL http://adsabs.harvard.edu/abs/2009LNCS.5567.....T＄\delimiter"026E30F＄n http://link.springer.com/10.1007/978-3-642-24785-9. doi: 10.1007/978-3-319-18461-6_17.  Google Scholar

[34]

E. Malone, M. Jehl, S. Arridge, T. Betcke and D. Holder, Stroke type differentiation using spectrally constrained multifrequency eit: Evaluation of feasibility in a realistic head model, Physiological Measurement, 35 (2014), 1051. doi: 10.1088/0967-3334/35/6/1051.  Google Scholar

[35]

N. Mandache, Exponential instability in an inverse problem for the schrödinger equation, Inverse Problems, 17 (2001), 1435-1444.  doi: 10.1088/0266-5611/17/5/313.  Google Scholar

[36]

M. Möller, T. Wittman, A. L. Bertozzi and M. Burger, A variational approach for sharpening high dimensional images, SIAM Journal on Imaging Sciences, 5 (2012), 150-178.  doi: 10.1137/100810356.  Google Scholar

[37]

J. Nocedal and S. J. Wright, Numerical Optimization, 2nd edition, Springer, New York, NY, USA, 2006.  Google Scholar

[38]

J. Rasch, V. Kolehmainen, R. Nivajrvi, M. Kettunen, O. Grhn, M. Burger and E.-M. Brinkmann, Dynamic mri reconstruction from undersampled data with an anatomical prescan, Inverse Problems, 34 (2018), 074001, 30pp, URL http://stacks.iop.org/0266-5611/34/i=/a=074001. doi: 10.1088/1361-6420/aac3af.  Google Scholar

[39]

E. Somersalo, M. Cheney and D. Isaacson, Existence and uniqueness for electrode models for electric current computed tomography, SIAM J. Appl. Math., 52 (1992), 1023–1040. doi: 10.1137/0152060.  Google Scholar

[40]

D. M. Strong, P. Blomgren and T. F. Chan, Spatially adaptive local feature-driven total variation minimizing image restoration, in Statistical and Stochastic Methods in Image Processing II, vol. 3167 of Proc. SPIE, 1997,222–233. Google Scholar

[41]

M. Vauhkonen, D. Vadasz, J. P. Kaipio, E. Somersalo and P. Karjalainen, Tikhonov regularization and prior information in electrical impedance tomography, IEEE Transactions on Medical Imaging, 17 (1998), 285-293.  doi: 10.1109/42.700740.  Google Scholar

[42]

C. R. Vogel, Computational Methods for Inverse Problems, SIAM, 2002. doi: 10.1137/1.9780898717570.  Google Scholar

[43]

K. Vunckx, A. Atre, K. Baete, A. Reilhac, C. M. Deroose, K. Van Laere and J. Nuyts, Evaluation of three MRI-based anatomical priors for quantitative PET brain imaging, IEEE Transactions on Medical Imaging, 31 (2012), 599-612.  doi: 10.1109/TMI.2011.2173766.  Google Scholar

show all references

References:
[1]

G. Alessandrini, Stable determination of conductivity by boundary measurements, Applicable Analysis, 27 (1988), 153-172.  doi: 10.1080/00036818808839730.  Google Scholar

[2]

M. Alsaker and J. Mueller, A d-bar algorithm with a priori information for 2-dimensional electrical impedance tomography, SIAM Journal on Imaging Sciences, 9 (2016), 1619-1654.  doi: 10.1137/15M1020137.  Google Scholar

[3]

S. R. Arridge, V. Kolehmainen and M. J. Schweiger, Reconstruction and Regularisation in Optical Tomography, in Interdisciplinary Workshop on Mathematical Methods in Biomedical Imaging and Intensity-Modulated Radiation, 2007. Google Scholar

[4]

B. Bai, Q. Li and R. M. Leahy, Magnetic Resonance-guided Positron Emission Tomography Image Reconstruction, Seminars in Nuclear Medicine, 43 (2013), 30-44.  doi: 10.1053/j.semnuclmed.2012.08.006.  Google Scholar

[5]

C. Ballester, V. Caselles, L. Igual, J. Verdera and B. Rougé, A Variational Model for P+XS Image Fusion, International Journal of Computer Vision, 69 (2006), 43-58.  doi: 10.1007/s11263-006-6852-x.  Google Scholar

[6]

C. Bathke, T. Kluth, C. Brandt and P. Maa, Improved image reconstruction in magnetic particle imaging using structural a priori information, International Journal on Magnetic Particle Imaging, 3, URL https://journal.iwmpi.org/index.php/iwmpi/article/view/64. Google Scholar

[7]

I. Bayram and M. E. Kamasak, A Directional Total Variation, IEEE Signal Processing Letters, 19 (2012), 781-784.  doi: 10.1109/LSP.2012.2220349.  Google Scholar

[8]

A. Beck and M. Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems, SIAM Journal on Imaging Sciences, 2 (2009), 183-202.  doi: 10.1137/080716542.  Google Scholar

[9]

A. Björck, Numerical Methods for Least Squares Problems, Society for Industrial and Applied Mathematics (SIAM), Philadelphia, PA, 1996. doi: 10.1137/1.9781611971484.  Google Scholar

[10]

J. E. Bowsher, H. Yuan, L. W. Hedlund, T. G. Turkington, G. Akabani, A. Badea, W. C. Kurylo, C. T. Wheeler, G. P. Cofer, M. W. Dewhirst and G. A. Johnson, Utilizing MRI Information to Estimate F18-FDG Distributions in Rat Flank Tumors, in IEEE Nuclear Science Symposium and Medical Imaging Conference, 2004, 2488–2492, http://ieeexplore.ieee.org/xpl/login.jsp?arnumber=1462760 & http://ieeexplore.ieee.org/xpls/abs{_}all.jsp?arnumber=1462760. doi: 10.1109/NSSMIC.2004.1462760.  Google Scholar

[11]

K. Bredies, Y. Dong and M. Hintermüller, Spatially dependent regularization parameter selection in total generalized variation models for image restoration, International Journal of Computer Mathematics, 90 (2013), 109-123.  doi: 10.1080/00207160.2012.700400.  Google Scholar

[12]

L. Bungert, D. A. Coomes, M. J. Ehrhardt, J. Rasch, R. Reisenhofer and C.-B. Schönlieb, Blind image fusion for hyperspectral imaging with the directional total variation, Inverse Problems, 34 (2008), 044003, 23 pp, URL http://arXiv.org/abs/1710.05705. doi: 10.1088/1361-6420/aaaf63.  Google Scholar

[13]

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging, Journal of Mathematical Imaging and Vision, 40 (2011), 120-145.  doi: 10.1007/s10851-010-0251-1.  Google Scholar

[14]

K.-S. Cheng, D. Isaacson, J. Newell and D. G. Gisser, Electrode models for electric current computed tomography, IEEE Transactions Biomed. Eng., 36 (1989), 918–924. Google Scholar

[15]

S. R. Cherry, Multimodality in vivo imaging systems: twice the power or double the trouble?, Annual Review of Biomedical Engineering, 8 (2006), 35-62.  doi: 10.1146/annurev.bioeng.8.061505.095728.  Google Scholar

[16]

T. Dowrick, C. Blochet and D. Holder, In vivo bioimpedance measurement of healthy and ischaemic rat brain: implications for stroke imaging using electrical impedance tomography, Physiological Measurement, 36 (2015), 1273, http://stacks.iop.org/0967-3334/36/i=6/a=1273. Google Scholar

[17]

M. J. Ehrhardt and M. M. Betcke, Multi-contrast MRI reconstruction with structure-guided total variation, SIAM Journal on Imaging Sciences, 9 (2016), 1084-1106.  doi: 10.1137/15M1047325.  Google Scholar

[18]

M. J. Ehrhardt, P. Markiewicz, M. Liljeroth, A. Barnes, V. Kolehmainen, J. Duncan, L. Pizarro, D. Atkinson, B. F. Hutton, S. Ourselin, K. Thielemans and S. R. Arridge, PET reconstruction with an anatomical MRI Prior using parallel level sets, IEEE Transactions on Medical Imaging, 35 (2016), 2189-2199, URL http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7452643. doi: 10.1109/TMI.2016.2549601.  Google Scholar

[19]

V. Estellers, S. Soatto and X. Bresson, Adaptive Regularization With the Structure Tensor, IEEE Transactions on Image Processing, 24 (2015), 1777-1790.  doi: 10.1109/TIP.2015.2409562.  Google Scholar

[20]

F. Fang, F. Li, C. Shen and G. Zhang, A Variational Approach for Pan-Sharpening, IEEE Transactions on Image Processing, 22 (2013), 2822-2834.  doi: 10.1109/TIP.2013.2258355.  Google Scholar

[21]

I. Frerichs, M. B. P. Amato, A. H. van Kaam, D. G. Tingay, Z. Zhao, B. Grychtol, M. Bodenstein, H. Gagnon, S. H. Bohm, E. Teschner, O. Stenqvist, T. Mauri, V. Torsani, L. Camporota, A. Schibler, G. K. Wolf, D. Gommers, S. Leonhardt and A. Adler, Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the translational eit development study group, Thorax, 72 (2017), 83-93, URL http://thorax.bmj.com/content/72/1/83. doi: 10.1136/thoraxjnl-2016-208357.  Google Scholar

[22]

M. Grasmair, Locally adaptive total variation regularization, in SSVM, LNCS, 5567 (2009), 331–342. doi: 10.1007/978-3-642-02256-2_28.  Google Scholar

[23]

M. Grasmair and F. Lenzen, Anisotropic total variation filtering, Optimization, 62 (2010), 323-339.  doi: 10.1007/s00245-010-9105-x.  Google Scholar

[24]

L. M. Heikkinen, T. Vilhunen, R. M. West and M. Vauhkonen, Simultaneous reconstruction of electrode contact impedances and internal electrical properties: Ii. laboratory experiments, Meas. Sci. Technol., 13 (2002), 1855-1861.  doi: 10.1088/0957-0233/13/12/308.  Google Scholar

[25]

B. F. Hutton, B. A. Thomas, K. Erlandsson, A. Bousse, A. Reilhac-Laborde, D. Kazantsev, S. Pedemonte, K. Vunckx, S. R. Arridge and S. Ourselin, What Approach to Brain Partial Volume Correction is best for PET/MRI?, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 702 (2013), 29-33.  doi: 10.1016/j.nima.2012.07.059.  Google Scholar

[26]

J. P. Kaipio, V. Kolehmainen, E. Somersalo and M. Vauhkonen, Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography, Inv Probl, 16 (2000), 1487-1522.  doi: 10.1088/0266-5611/16/5/321.  Google Scholar

[27]

J. P. Kaipio, V. Kolehmainen, M. Vauhkonen and E. Somersalo, Inverse problems with structural prior information, Inverse Problems, 15 (1999), 713-729.  doi: 10.1088/0266-5611/15/3/306.  Google Scholar

[28]

D. Kazantsev, W. R. B. Lionheart, P. J. Withers and P. D. Lee, Multimodal Image Reconstruction using Supplementary Structural Information in Total Variation Regularization, Sensing and Imaging, 15 (2014), 97. doi: 10.1007/s11220-014-0097-5.  Google Scholar

[29]

F. Knoll, Y. Dong, C. Langskammer, M. Hintermüller and R. Stollberger, Total variation denoising with spatially dependent regularization, in Proc. Intl. Soc. Mag. Reson. Med., 18 (2010), 5088. Google Scholar

[30]

V. Kolehmainen, M. Lassas and P. Ola, Electrical impedance tomography problem with inaccurately known boundary and contact impedances, IEEE Transactions on Medical Imaging, 27 (2008), 1404-1414.  doi: 10.1109/ISBI.2006.1625120.  Google Scholar

[31]

J. Kourunen, T. Savolainen, A. Lehikoinen, M. Vauhkonen and L. M. Heikkinen, Suitability of a pxi platform for an electrical impedance tomography system, Meas. Sci. Technol., 20 (2009), 015503. doi: 10.1088/0957-0233/20/1/015503.  Google Scholar

[32]

R. M. Leahy and X. Yan, Incorporation of Anatomical MR Data for Improved Functional Imaging with PET, in Information Processing in Medical Imaging, Springer, 1991,105–120. doi: 10.1007/BFb0033746.  Google Scholar

[33]

F. Lenzen and J. Berger, Solution-driven adaptive total variation regularization, in SSVM, 9087 (2015), 203-215, URL http://adsabs.harvard.edu/abs/2009LNCS.5567.....T＄\delimiter"026E30F＄n http://link.springer.com/10.1007/978-3-642-24785-9. doi: 10.1007/978-3-319-18461-6_17.  Google Scholar

[34]

E. Malone, M. Jehl, S. Arridge, T. Betcke and D. Holder, Stroke type differentiation using spectrally constrained multifrequency eit: Evaluation of feasibility in a realistic head model, Physiological Measurement, 35 (2014), 1051. doi: 10.1088/0967-3334/35/6/1051.  Google Scholar

[35]

N. Mandache, Exponential instability in an inverse problem for the schrödinger equation, Inverse Problems, 17 (2001), 1435-1444.  doi: 10.1088/0266-5611/17/5/313.  Google Scholar

[36]

M. Möller, T. Wittman, A. L. Bertozzi and M. Burger, A variational approach for sharpening high dimensional images, SIAM Journal on Imaging Sciences, 5 (2012), 150-178.  doi: 10.1137/100810356.  Google Scholar

[37]

J. Nocedal and S. J. Wright, Numerical Optimization, 2nd edition, Springer, New York, NY, USA, 2006.  Google Scholar

[38]

J. Rasch, V. Kolehmainen, R. Nivajrvi, M. Kettunen, O. Grhn, M. Burger and E.-M. Brinkmann, Dynamic mri reconstruction from undersampled data with an anatomical prescan, Inverse Problems, 34 (2018), 074001, 30pp, URL http://stacks.iop.org/0266-5611/34/i=/a=074001. doi: 10.1088/1361-6420/aac3af.  Google Scholar

[39]

E. Somersalo, M. Cheney and D. Isaacson, Existence and uniqueness for electrode models for electric current computed tomography, SIAM J. Appl. Math., 52 (1992), 1023–1040. doi: 10.1137/0152060.  Google Scholar

[40]

D. M. Strong, P. Blomgren and T. F. Chan, Spatially adaptive local feature-driven total variation minimizing image restoration, in Statistical and Stochastic Methods in Image Processing II, vol. 3167 of Proc. SPIE, 1997,222–233. Google Scholar

[41]

M. Vauhkonen, D. Vadasz, J. P. Kaipio, E. Somersalo and P. Karjalainen, Tikhonov regularization and prior information in electrical impedance tomography, IEEE Transactions on Medical Imaging, 17 (1998), 285-293.  doi: 10.1109/42.700740.  Google Scholar

[42]

C. R. Vogel, Computational Methods for Inverse Problems, SIAM, 2002. doi: 10.1137/1.9780898717570.  Google Scholar

[43]

K. Vunckx, A. Atre, K. Baete, A. Reilhac, C. M. Deroose, K. Van Laere and J. Nuyts, Evaluation of three MRI-based anatomical priors for quantitative PET brain imaging, IEEE Transactions on Medical Imaging, 31 (2012), 599-612.  doi: 10.1109/TMI.2011.2173766.  Google Scholar

Figure 1.  Numerical experiment (Case 1). Rows top to bottom: $ \sigma_{{\rm true}} $ and reference images $ p(r) $ (top row), weighting function $ \gamma(r) $ (second row), reconstructions using $ {\rm SH}_1 $ regularisation (third row) and STV regularisation (fourth row)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 2.  Plot of standard deviation with respect to bias of the reconstructed conductivity with different values of the regularisation parameter $ \alpha $ for the simulation in figure 1. The left image shows the curves for the mean conductivity in the area of the true inclusion on the top in $ {\sigma}_{\rm true} $, and the image on the right for the inclusion on the bottom right. The triangle denotes the point corresponding to the smallest value of $ \alpha $ in the curves
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 3.  Numerical experiment (Case 2). Rows top to bottom: $ \sigma_{{\rm true}} $ and reference images $ p(r) $ (top row), weighting function $ \gamma(r) $ (second row), reconstructions using $ {\rm SH}_1 $ regularisation (third row) and STV regularisation (fourth row)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 4.  Numerical experiment (Case 3). Rows top to bottom: $ \sigma_{{\rm true}} $ and reference images $ p(r) $ (top row), weighting function $ \gamma(r) $ (second row), reconstructions using $ {\rm SH}_1 $ regularisation (third row) and STV regularisation (fourth row)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 5.  Numerical experiment (Case 3): Reconstructions with increasing uncertainty about the edge location in the partial edge information. Rows top to bottom: $ \sigma_{{\rm true}} $ and reference images $ p(r) $ (top row), weighting function $ \gamma(r) $ (second row), reconstructions using $ {\rm SH}_1 $ regularisation (third row) and STV regularisation (fourth row)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 6.  Numerical experiment (Case 4). Rows top to bottom: $ \sigma_{{\rm true}} $ and reference images $ p(r) $ (top row), weighting function $ \gamma(r) $ (second row), reconstructions using $ {\rm SH}_1 $ regularisation (third row) and STV regularisation (fourth row)
Figure Options

Download full-size image

Download as PowerPoint slide

Figure 7.  Physical experiment. Top section: Photograph of the target and the reference images $ p(r) $. The second row shows the weighting functions $ \gamma(r) $. Bottom section: Reconstructions. $ {\rm SH}_1 $ regularisation (third row), STV regularisation (fourth row). (Color scales of the reconstructions are arbitrary in the sense that they are reconstructed 2D values from 3D data)
Figure Options

Download full-size image

Download as PowerPoint slide

Table 1.  Examples of $ \psi $ for different regularisation schemes in variational form. $ \psi $ is the mapping which defines the penalty for the gradient magnitude in (4) and $ \kappa $ is the corresponding local diffusivity function in (6).
$ \psi(t) $ $ \kappa(t) $
$ 1^{\rm st} $-order Tikhonov $ \frac{ t ^2}2 $ $ 1 $
TV $ t $ $ \frac{1}{ t } $
Smoothed TV $ T\left( t ^2 + T^2\right)^{1/2} -T^2 $ $ T \left( t ^2 + T^2\right)^{-1/2} $
Perona-Malik (1) $ \frac{T^2}2 \log\left(1 + \frac{ t ^2}{T^2}\right) $ $ T^2 \left( t ^2 + T^2\right)^{-1} $
Perona-Malik (2) $ \frac{T^2}2 \left[1 - \exp\left(-\frac{ t ^2}{T^2}\right)\right] $ $ \exp\left(- \frac{ t ^2}{T^2}\right) $
Huber $\left\{ \begin{array}{l} Tt - \frac{{{T^2}}}{2}\\ \frac{{{t^2}}}{2} \end{array} \right.$ $ \frac{T}{ t } $
1		 if $ t > T $
else
Tukey $\left\{ \begin{array}{l} \frac{{{T^2}}}{6}\\ \frac{{{T^2}}}{6}\left[ {1 - {{\left( {1 - \frac{{{t^2}}}{{{T^2}}}} \right)}^3}} \right] \end{array} \right.$ $ 0 $
$ \left(1 - \frac{ t ^2}{T^2}\right)^2 $ 		if $ t> T $
else
Table Options

Download as excel

$ \psi(t) $ $ \kappa(t) $
$ 1^{\rm st} $-order Tikhonov $ \frac{ t ^2}2 $ $ 1 $
TV $ t $ $ \frac{1}{ t } $
Smoothed TV $ T\left( t ^2 + T^2\right)^{1/2} -T^2 $ $ T \left( t ^2 + T^2\right)^{-1/2} $
Perona-Malik (1) $ \frac{T^2}2 \log\left(1 + \frac{ t ^2}{T^2}\right) $ $ T^2 \left( t ^2 + T^2\right)^{-1} $
Perona-Malik (2) $ \frac{T^2}2 \left[1 - \exp\left(-\frac{ t ^2}{T^2}\right)\right] $ $ \exp\left(- \frac{ t ^2}{T^2}\right) $
Huber $\left\{ \begin{array}{l} Tt - \frac{{{T^2}}}{2}\\ \frac{{{t^2}}}{2} \end{array} \right.$ $ \frac{T}{ t } $
1		 if $ t > T $
else
Tukey $\left\{ \begin{array}{l} \frac{{{T^2}}}{6}\\ \frac{{{T^2}}}{6}\left[ {1 - {{\left( {1 - \frac{{{t^2}}}{{{T^2}}}} \right)}^3}} \right] \end{array} \right.$ $ 0 $
$ \left(1 - \frac{ t ^2}{T^2}\right)^2 $ 		if $ t> T $
else
Table 2.  Reconstruction errors (28) for the simulated test cases for varying regularizations ($ {\rm SH}_1 $, STV) and reference images (no structure, correct, partial). Cases 1-4 refer to reconstructions in the figures 1, 3, 4 and 6 respectively. Errors are given in percentages
SH1 STV
case no structure correct partial no structure correct partial
1 12.3 3.6 8.6 8.8 3.5 4.9
2 15.6 5.6 10.9 13.8 3.3 10.1
3 10.6 3.5 6.5 8.0 2.4 4.9
4 15.3 11.5 12.9 14.3 11.1 12.7
Table Options

Download as excel

SH1 STV
case no structure correct partial no structure correct partial
1 12.3 3.6 8.6 8.8 3.5 4.9
2 15.6 5.6 10.9 13.8 3.3 10.1
3 10.6 3.5 6.5 8.0 2.4 4.9
4 15.3 11.5 12.9 14.3 11.1 12.7
[1]

Ke Zhang, Maokun Li, Fan Yang, Shenheng Xu, Aria Abubakar. Electrical impedance tomography with multiplicative regularization. Inverse Problems & Imaging, 2019, 13 (6) : 1139-1159. doi: 10.3934/ipi.2019051
[2]

Henrik Garde, Kim Knudsen. 3D reconstruction for partial data electrical impedance tomography using a sparsity prior. Conference Publications, 2015, 2015 (special) : 495-504. doi: 10.3934/proc.2015.0495
[3]

Bastian Gebauer. Localized potentials in electrical impedance tomography. Inverse Problems & Imaging, 2008, 2 (2) : 251-269. doi: 10.3934/ipi.2008.2.251
[4]

Fabrice Delbary, Rainer Kress. Electrical impedance tomography using a point electrode inverse scheme for complete electrode data. Inverse Problems & Imaging, 2011, 5 (2) : 355-369. doi: 10.3934/ipi.2011.5.355
[5]

Hiroshi Isozaki. Inverse boundary value problems in the horosphere - A link between hyperbolic geometry and electrical impedance tomography. Inverse Problems & Imaging, 2007, 1 (1) : 107-134. doi: 10.3934/ipi.2007.1.107
[6]

Nuutti Hyvönen, Harri Hakula, Sampsa Pursiainen. Numerical implementation of the factorization method within the complete electrode model of electrical impedance tomography. Inverse Problems & Imaging, 2007, 1 (2) : 299-317. doi: 10.3934/ipi.2007.1.299
[7]

Helmut Harbrecht, Thorsten Hohage. A Newton method for reconstructing non star-shaped domains in electrical impedance tomography. Inverse Problems & Imaging, 2009, 3 (2) : 353-371. doi: 10.3934/ipi.2009.3.353
[8]

Sarah Jane Hamilton, Andreas Hauptmann, Samuli Siltanen. A data-driven edge-preserving D-bar method for electrical impedance tomography. Inverse Problems & Imaging, 2014, 8 (4) : 1053-1072. doi: 10.3934/ipi.2014.8.1053
[9]

Melody Alsaker, Sarah Jane Hamilton, Andreas Hauptmann. A direct D-bar method for partial boundary data electrical impedance tomography with a priori information. Inverse Problems & Imaging, 2017, 11 (3) : 427-454. doi: 10.3934/ipi.2017020
[10]

Kari Astala, Jennifer L. Mueller, Lassi Päivärinta, Allan Perämäki, Samuli Siltanen. Direct electrical impedance tomography for nonsmooth conductivities. Inverse Problems & Imaging, 2011, 5 (3) : 531-549. doi: 10.3934/ipi.2011.5.531
[11]

Ville Kolehmainen, Matti Lassas, Petri Ola, Samuli Siltanen. Recovering boundary shape and conductivity in electrical impedance tomography. Inverse Problems & Imaging, 2013, 7 (1) : 217-242. doi: 10.3934/ipi.2013.7.217
[12]

Mohsen Tadi. A computational method for an inverse problem in a parabolic system. Discrete & Continuous Dynamical Systems - B, 2009, 12 (1) : 205-218. doi: 10.3934/dcdsb.2009.12.205
[13]

Liliana Borcea, Fernando Guevara Vasquez, Alexander V. Mamonov. Study of noise effects in electrical impedance tomography with resistor networks. Inverse Problems & Imaging, 2013, 7 (2) : 417-443. doi: 10.3934/ipi.2013.7.417
[14]

Dong liu, Ville Kolehmainen, Samuli Siltanen, Anne-maria Laukkanen, Aku Seppänen. Estimation of conductivity changes in a region of interest with electrical impedance tomography. Inverse Problems & Imaging, 2015, 9 (1) : 211-229. doi: 10.3934/ipi.2015.9.211
[15]

Gen Nakamura, Päivi Ronkanen, Samuli Siltanen, Kazumi Tanuma. Recovering conductivity at the boundary in three-dimensional electrical impedance tomography. Inverse Problems & Imaging, 2011, 5 (2) : 485-510. doi: 10.3934/ipi.2011.5.485
[16]

Nicolay M. Tanushev, Luminita Vese. A piecewise-constant binary model for electrical impedance tomography. Inverse Problems & Imaging, 2007, 1 (2) : 423-435. doi: 10.3934/ipi.2007.1.423
[17]

Nuutti Hyvönen, Lassi Päivärinta, Janne P. Tamminen. Enhancing D-bar reconstructions for electrical impedance tomography with conformal maps. Inverse Problems & Imaging, 2018, 12 (2) : 373-400. doi: 10.3934/ipi.2018017
[18]

Kimmo Karhunen, Aku Seppänen, Jari P. Kaipio. Adaptive meshing approach to identification of cracks with electrical impedance tomography. Inverse Problems & Imaging, 2014, 8 (1) : 127-148. doi: 10.3934/ipi.2014.8.127
[19]

Jérémi Dardé, Harri Hakula, Nuutti Hyvönen, Stratos Staboulis. Fine-tuning electrode information in electrical impedance tomography. Inverse Problems & Imaging, 2012, 6 (3) : 399-421. doi: 10.3934/ipi.2012.6.399
[20]

Melody Dodd, Jennifer L. Mueller. A real-time D-bar algorithm for 2-D electrical impedance tomography data. Inverse Problems & Imaging, 2014, 8 (4) : 1013-1031. doi: 10.3934/ipi.2014.8.1013

2018 Impact Factor: 1.469

Tools

Metrics

  • PDF downloads (136)
  • HTML views (439)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2019 American Institute of Mathematical Sciences