5,441 research outputs found
EIT Reconstruction Algorithms: Pitfalls, Challenges and Recent Developments
We review developments, issues and challenges in Electrical Impedance
Tomography (EIT), for the 4th Workshop on Biomedical Applications of EIT,
Manchester 2003. We focus on the necessity for three dimensional data
collection and reconstruction, efficient solution of the forward problem and
present and future reconstruction algorithms. We also suggest common pitfalls
or ``inverse crimes'' to avoid.Comment: A review paper for the 4th Workshop on Biomedical Applications of
EIT, Manchester, UK, 200
Combining Radon transform and Electrical Capacitance Tomography for a imaging device
This paper describes a coplanar non invasive non destructive capacitive
imaging device. We first introduce a mathematical model for its output, and
discuss some of its theoretical capabilities. We show that the data obtained
from this device can be interpreted as a weighted Radon transform of the
electrical permittivity of the measured object near its surface. Image
reconstructions from experimental data provide good surface resolution as well
as short depth imaging, making the apparatus a imager. The quality of
the images leads us to expect that excellent results can be delivered by
\emph{ad-hoc} optimized inversion formulas. There are also interesting, yet
unexplored, theoretical questions on imaging that this sensor will allow to
test
A mathematical model and inversion procedure for Magneto-Acousto-Electric Tomography (MAET)
Magneto-Acousto-Electric Tomography (MAET), also known as the Lorentz force
or Hall effect tomography, is a novel hybrid modality designed to be a
high-resolution alternative to the unstable Electrical Impedance Tomography. In
the present paper we analyze existing mathematical models of this method, and
propose a general procedure for solving the inverse problem associated with
MAET. It consists in applying to the data one of the algorithms of
Thermo-Acoustic tomography, followed by solving the Neumann problem for the
Laplace equation and the Poisson equation.
For the particular case when the region of interest is a cube, we present an
explicit series solution resulting in a fast reconstruction algorithm. As we
show, both analytically and numerically, MAET is a stable technique yilelding
high-resolution images even in the presence of significant noise in the data
2D and 3D reconstructions in acousto-electric tomography
We propose and test stable algorithms for the reconstruction of the internal
conductivity of a biological object using acousto-electric measurements.
Namely, the conventional impedance tomography scheme is supplemented by
scanning the object with acoustic waves that slightly perturb the conductivity
and cause the change in the electric potential measured on the boundary of the
object. These perturbations of the potential are then used as the data for the
reconstruction of the conductivity. The present method does not rely on
"perfectly focused" acoustic beams. Instead, more realistic propagating
spherical fronts are utilized, and then the measurements that would correspond
to perfect focusing are synthesized. In other words, we use \emph{synthetic
focusing}. Numerical experiments with simulated data show that our techniques
produce high quality images, both in 2D and 3D, and that they remain accurate
in the presence of high-level noise in the data. Local uniqueness and stability
for the problem also hold
Study of noise effects in electrical impedance tomography with resistor networks
We present a study of the numerical solution of the two dimensional
electrical impedance tomography problem, with noisy measurements of the
Dirichlet to Neumann map. The inversion uses parametrizations of the
conductivity on optimal grids. The grids are optimal in the sense that finite
volume discretizations on them give spectrally accurate approximations of the
Dirichlet to Neumann map. The approximations are Dirichlet to Neumann maps of
special resistor networks, that are uniquely recoverable from the measurements.
Inversion on optimal grids has been proposed and analyzed recently, but the
study of noise effects on the inversion has not been carried out. In this paper
we present a numerical study of both the linearized and the nonlinear inverse
problem. We take three different parametrizations of the unknown conductivity,
with the same number of degrees of freedom. We obtain that the parametrization
induced by the inversion on optimal grids is the most efficient of the three,
because it gives the smallest standard deviation of the maximum a posteriori
estimates of the conductivity, uniformly in the domain. For the nonlinear
problem we compute the mean and variance of the maximum a posteriori estimates
of the conductivity, on optimal grids. For small noise, we obtain that the
estimates are unbiased and their variance is very close to the optimal one,
given by the Cramer-Rao bound. For larger noise we use regularization and
quantify the trade-off between reducing the variance and introducing bias in
the solution. Both the full and partial measurement setups are considered.Comment: submitted to Inverse Problems and Imagin
Comparison of linear and non-linear monotononicity-based shape reconstruction using exact matrix characterizations
Detecting inhomogeneities in the electrical conductivity is a special case of
the inverse problem in electrical impedance tomography, that leads to fast
direct reconstruction methods. One such method can, under reasonable
assumptions, exactly characterize the inhomogeneities based on monotonicity
properties of either the Neumann-to-Dirichlet map (non-linear) or its Fr\'echet
derivative (linear). We give a comparison of the non-linear and linear approach
in the presence of measurement noise, and show numerically that the two methods
give essentially the same reconstruction in the unit disk domain. For a fair
comparison, exact matrix characterizations are used when probing the
monotonicity relations to avoid errors from numerical solution to PDEs and
numerical integration. Using a special factorization of the
Neumann-to-Dirichlet map also makes the non-linear method as fast as the linear
method in the unit disk geometry.Comment: 18 pages, 5 figures, 1 tabl
Iterative Updating of Model Error for Bayesian Inversion
In computational inverse problems, it is common that a detailed and accurate
forward model is approximated by a computationally less challenging substitute.
The model reduction may be necessary to meet constraints in computing time when
optimization algorithms are used to find a single estimate, or to speed up
Markov chain Monte Carlo (MCMC) calculations in the Bayesian framework. The use
of an approximate model introduces a discrepancy, or modeling error, that may
have a detrimental effect on the solution of the ill-posed inverse problem, or
it may severely distort the estimate of the posterior distribution. In the
Bayesian paradigm, the modeling error can be considered as a random variable,
and by using an estimate of the probability distribution of the unknown, one
may estimate the probability distribution of the modeling error and incorporate
it into the inversion. We introduce an algorithm which iterates this idea to
update the distribution of the model error, leading to a sequence of posterior
distributions that are demonstrated empirically to capture the underlying truth
with increasing accuracy. Since the algorithm is not based on rejections, it
requires only limited full model evaluations.
We show analytically that, in the linear Gaussian case, the algorithm
converges geometrically fast with respect to the number of iterations. For more
general models, we introduce particle approximations of the iteratively
generated sequence of distributions; we also prove that each element of the
sequence converges in the large particle limit. We show numerically that, as in
the linear case, rapid convergence occurs with respect to the number of
iterations. Additionally, we show through computed examples that point
estimates obtained from this iterative algorithm are superior to those obtained
by neglecting the model error.Comment: 39 pages, 9 figure
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