RT Journal Article
ID 01e64edd0e0cf5d3
A1 Nissinen, Antti
A1 Kolehmainen, Ville
A1 Kaipio, Jari P.
T1 RECONSTRUCTION OF DOMAIN BOUNDARY AND CONDUCTIVITY IN ELECTRICAL IMPEDANCE TOMOGRAPHY USING THE APPROXIMATION ERROR APPROACH
JF International Journal for Uncertainty Quantification
JO IJUQ
YR 2011
FD 2011-06-22
VO 1
IS 3
SP 203
OP 222
K1 inverse problems
K1 electrical impedance tomography
K1 statistical inversion
K1 modelling errors,
inaccurately known boundary
K1 model reduction
AB Electrical impedance tomography (EIT) is a highly unstable problem with respect to measurement and modeling errors.
With clinical measurements, knowledge about the body shape is usually uncertain. Since the use of an incorrect model
domain in the measurement model is bound to lead to severe estimation errors, one possibility is to estimate both the
conductivity and parametrization of the domain boundary. This could in principle be carried out using the Bayesian
inversion paradigm and Markov chain Monte Carlo sampling, but such an approach would lead in clinical situation to
an impractical solution because of the excessive computational complexity. In this paper, we adapt the so-called approximation error approach for approximate recovery of the domain boundary and the conductivity. In the approximation
error approach, the modeling error caused by an inaccurately known boundary is treated as an auxiliary noise process
in the measurement model and sample statistics for the noise process are estimated based on the prior models of the
conductivity and boundary shape. Using the approximation error model, we reconstruct the conductivity and a low
rank approximation for the realization of the modeling error, and then recover an approximation for the domain boundary using the joint distribution of the modeling error and the boundary parametrization. We also compute approximate
spread estimates for the reconstructed boundary. We evaluate the approach with simulated examples of thorax imaging
and also with experimental data from a laboratory setting. The reconstructed boundaries and posterior uncertainty are
feasible; in particular, the actual domain boundaries are essentially within the posterior spread estimates.
PB Begell House
LK http://dl.begellhouse.com/journals/52034eb04b657aea,2bc4b62a46b8e5f8,01e64edd0e0cf5d3.html