35 research outputs found
Convergence and regularization for monotonicity-based shape reconstruction in electrical impedance tomography
The inverse problem of electrical impedance tomography is severely ill-posed,
meaning that, only limited information about the conductivity can in practice
be recovered from boundary measurements of electric current and voltage.
Recently it was shown that a simple monotonicity property of the related
Neumann-to-Dirichlet map can be used to characterize shapes of inhomogeneities
in a known background conductivity. In this paper we formulate a
monotonicity-based shape reconstruction scheme that applies to approximative
measurement models, and regularizes against noise and modelling error. We
demonstrate that for admissible choices of regularization parameters the
inhomogeneities are detected, and under reasonable assumptions, asymptotically
exactly characterized. Moreover, we rigorously associate this result with the
complete electrode model, and describe how a computationally cheap
monotonicity-based reconstruction algorithm can be implemented. Numerical
reconstructions from both simulated and real-life measurement data are
presented
Monotonicity and enclosure methods for the p-Laplace equation
We show that the convex hull of a monotone perturbation of a homogeneous
background conductivity in the -conductivity equation is determined by
knowledge of the nonlinear Dirichlet-Neumann operator. We give two independent
proofs, one of which is based on the monotonicity method and the other on the
enclosure method. Our results are constructive and require no jump or
smoothness properties on the conductivity perturbation or its support.Comment: 18 page
Distinguishability revisited: depth dependent bounds on reconstruction quality in electrical impedance tomography
The reconstruction problem in electrical impedance tomography is highly
ill-posed, and it is often observed numerically that reconstructions have poor
resolution far away from the measurement boundary but better resolution near
the measurement boundary. The observation can be quantified by the concept of
distinguishability of inclusions. This paper provides mathematically rigorous
results supporting the intuition. Indeed, for a model problem lower and upper
bounds on the distinguishability of an inclusion are derived in terms of the
boundary data. These bounds depend explicitly on the distance of the inclusion
to the boundary, i.e. the depth of the inclusion. The results are obtained for
disk inclusions in a homogeneous background in the unit disk. The theoretical
bounds are verified numerically using a novel, exact characterization of the
forward map as a tridiagonal matrix.Comment: 25 pages, 6 figure
The regularized monotonicity method: detecting irregular indefinite inclusions
In inclusion detection in electrical impedance tomography, the support of
perturbations (inclusion) from a known background conductivity is typically
reconstructed from idealized continuum data modelled by a Neumann-to-Dirichlet
map. Only few reconstruction methods apply when detecting indefinite
inclusions, where the conductivity distribution has both more and less
conductive parts relative to the background conductivity; one such method is
the monotonicity method of Harrach, Seo, and Ullrich. We formulate the method
for irregular indefinite inclusions, meaning that we make no regularity
assumptions on the conductivity perturbations nor on the inclusion boundaries.
We show, provided that the perturbations are bounded away from zero, that the
outer support of the positive and negative parts of the inclusions can be
reconstructed independently. Moreover, we formulate a regularization scheme
that applies to a class of approximative measurement models, including the
Complete Electrode Model, hence making the method robust against modelling
error and noise. In particular, we demonstrate that for a convergent family of
approximative models there exists a sequence of regularization parameters such
that the outer shape of the inclusions is asymptotically exactly characterized.
Finally, a peeling-type reconstruction algorithm is presented and, for the
first time in literature, numerical examples of monotonicity reconstructions
for indefinite inclusions are presented.Comment: 28 pages, 7 figure
Reconstruction of piecewise constant layered conductivities in electrical impedance tomography
This work presents a new constructive uniqueness proof for Calder\'on's
inverse problem of electrical impedance tomography, subject to local Cauchy
data, for a large class of piecewise constant conductivities that we call
"piecewise constant layered conductivities" (PCLC). The resulting
reconstruction method only relies on the physically intuitive monotonicity
principles of the local Neumann-to-Dirichlet map, and therefore the method
lends itself well to efficient numerical implementation and generalization to
electrode models. Several direct reconstruction methods exist for the related
problem of inclusion detection, however they share the property that "holes in
inclusions" or "inclusions-within-inclusions" cannot be determined. One such
method is the monotonicity method of Harrach, Seo, and Ullrich, and in fact the
method presented here is a modified variant of the monotonicity method which
overcomes this problem. More precisely, the presented method abuses that a PCLC
type conductivity can be decomposed into nested layers of positive and/or
negative perturbations that, layer-by-layer, can be determined via the
monotonicity method. The conductivity values on each layer are found via basic
one-dimensional optimization problems constrained by monotonicity relations.Comment: 12 pages, 1 figur