Sparsity reconstruction in electrical impedance tomography: An experimental evaluation
Kaipio, Jari P.
KAUST Grant NumberKUS-C1-016-04
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AbstractWe investigate the potential of sparsity constraints in the electrical impedance tomography (EIT) inverse problem of inferring the distributed conductivity based on boundary potential measurements. In sparsity reconstruction, inhomogeneities of the conductivity are a priori assumed to be sparse with respect to a certain basis. This prior information is incorporated into a Tikhonov-type functional by including a sparsity-promoting ℓ1-penalty term. The functional is minimized with an iterative soft shrinkage-type algorithm. In this paper, the feasibility of the sparsity reconstruction approach is evaluated by experimental data from water tank measurements. The reconstructions are computed both with sparsity constraints and with a more conventional smoothness regularization approach. The results verify that the adoption of ℓ1-type constraints can enhance the quality of EIT reconstructions: in most of the test cases the reconstructions with sparsity constraints are both qualitatively and quantitatively more feasible than that with the smoothness constraint. © 2011 Elsevier B.V. All rights reserved.
CitationGehre M, Kluth T, Lipponen A, Jin B, Seppänen A, et al. (2012) Sparsity reconstruction in electrical impedance tomography: An experimental evaluation. Journal of Computational and Applied Mathematics 236: 2126–2136. Available: http://dx.doi.org/10.1016/j.cam.2011.09.035.
SponsorsThe work of BJ was substantially supported by the Alexander von Humboldt Foundation through a postdoctoral researcher fellowship and partially supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST), and that of PM was supported by the German Science Foundation through grant MA 1657/18-1. AL, AS and JK were supported by the Academy of Finland (application number 213476, Finnish Programme for Centres of Excellence in Research 2006-2011), TEKES (Contract No. 40370/06), Finnish Doctoral Programme in Computational Sciences and University of Auckland, Faculty of Science FDRF project 3624414/9844. The authors are grateful to an anonymous referee, whose comments helped clarify several ambiguities.