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

Ville Kolehmainen; Matthias J. Ehrhardt; Simon R. Arridge

doi:10.3934/ipi.2019015

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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)

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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

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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)

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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)

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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)

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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)

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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)

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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

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	$ \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

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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

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American Institute of Mathematical Sciences

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

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

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