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

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.

[1]  R. Stollberger,et al.  Total Variation Denoising with Spatially Dependent Regularization , 2009 .

[2]  Xavier Bresson,et al.  Adaptive Regularization With the Structure Tensor , 2015, IEEE Transactions on Image Processing.

[3]  Jari P. Kaipio,et al.  Tikhonov regularization and prior information in electrical impedance tomography , 1998, IEEE Transactions on Medical Imaging.

[4]  E. Somersalo,et al.  Inverse problems with structural prior information , 1999 .

[5]  Anthonin Reilhac,et al.  Evaluation of Three MRI-Based Anatomical Priors for Quantitative PET Brain Imaging , 2012, IEEE Transactions on Medical Imaging.

[6]  Yiqiu Dong,et al.  Spatially dependent regularization parameter selection in total generalized variation models for image restoration , 2013, Int. J. Comput. Math..

[7]  Tony F. Chan,et al.  Spatially adaptive local-feature-driven total variation minimizing image restoration , 1997, Optics & Photonics.

[8]  Giovanni Alessandrini,et al.  Stable determination of conductivity by boundary measurements , 1988 .

[9]  Marko Vauhkonen,et al.  Simultaneous reconstruction of electrode contact impedances and internal electrical properties: II. Laboratory experiments , 2002 .

[10]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[11]  Simon Arridge,et al.  Stroke type differentiation using spectrally constrained multifrequency EIT: evaluation of feasibility in a realistic head model , 2014, Physiological measurement.

[12]  Michael Möller,et al.  A Variational Approach for Sharpening High Dimensional Images , 2012, SIAM J. Imaging Sci..

[13]  C Blochet,et al.  In vivo bioimpedance measurement of healthy and ischaemic rat brain: implications for stroke imaging using electrical impedance tomography , 2015, Physiological measurement.

[14]  Johannes Berger,et al.  Solution-Driven Adaptive Total Variation Regularization , 2015, SSVM.

[15]  D. Isaacson,et al.  Electrode models for electric current computed tomography , 1989, IEEE Transactions on Biomedical Engineering.

[16]  Julian Rasch,et al.  Dynamic MRI reconstruction from undersampled data with an anatomical prescan , 2017, Inverse Problems.

[17]  Carola-Bibiane Schönlieb,et al.  Blind image fusion for hyperspectral imaging with the directional total variation , 2017, ArXiv.

[18]  Ville Kolehmainen,et al.  Electrical Impedance Tomography Problem With Inaccurately Known Boundary and Contact Impedances , 2006, IEEE Transactions on Medical Imaging.

[19]  Markus Grasmair,et al.  Locally Adaptive Total Variation Regularization , 2009, SSVM.

[20]  Ilker Bayram,et al.  Directional Total Variation , 2012, IEEE Signal Processing Letters.

[21]  S. Cherry Multimodality in vivo imaging systems: twice the power or double the trouble? , 2006, Annual review of biomedical engineering.

[22]  Fang Li,et al.  A Variational Approach for Pan-Sharpening , 2013, IEEE Transactions on Image Processing.

[23]  Simon R. Arridge,et al.  What approach to brain partial volume correction is best for PET/MRI? , 2013 .

[24]  Marko Vauhkonen,et al.  Suitability of a PXI platform for an electrical impedance tomography system , 2008 .

[25]  E. Somersalo,et al.  Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography , 2000 .

[26]  Jennifer L. Mueller,et al.  A D-Bar Algorithm with A Priori Information for 2-Dimensional Electrical Impedance Tomography , 2016, SIAM J. Imaging Sci..

[27]  Niculae Mandache,et al.  Exponential instability in an inverse problem for the Schrodinger equation , 2001 .

[28]  R. Leahy,et al.  Magnetic resonance-guided positron emission tomography image reconstruction. , 2013, Seminars in nuclear medicine.

[29]  C. Vogel Computational Methods for Inverse Problems , 1987 .

[30]  J. Bowsher,et al.  Utilizing MRI information to estimate F18-FDG distributions in rat flank tumors , 2004, IEEE Symposium Conference Record Nuclear Science 2004..

[31]  Tobias Kluth,et al.  Improved image reconstruction in magnetic particle imaging using structural a priori information , 2017 .

[32]  Arridge,et al.  Reconstruction and Regularisation in Optical Tomography , 2008 .

[33]  Marta M. Betcke,et al.  Multicontrast MRI Reconstruction with Structure-Guided Total Variation , 2015, SIAM J. Imaging Sci..

[34]  M. Grasmair,et al.  Anisotropic Total Variation Filtering , 2010 .

[35]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[36]  Steffen Leonhardt,et al.  Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group , 2016, Thorax.

[37]  Pawel Markiewicz,et al.  PET Reconstruction With an Anatomical MRI Prior Using Parallel Level Sets , 2016, IEEE Transactions on Medical Imaging.

[38]  Laura Igual,et al.  A Variational Model for P+XS Image Fusion , 2006, International Journal of Computer Vision.

[39]  J. Navarro-Pedreño Numerical Methods for Least Squares Problems , 1996 .

[40]  Philip J. Withers,et al.  Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization , 2014, Sensing and imaging.

[41]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[42]  E. Somersalo,et al.  Existence and uniqueness for electrode models for electric current computed tomography , 1992 .

[43]  Richard M. Leahy,et al.  Incorporation of Anatomical MR Data for Improved Dunctional Imaging with PET , 1991, IPMI.