7 research outputs found
Computational framework for applying electrical impedance tomography to head imaging
This work introduces a computational framework for applying absolute
electrical impedance tomography to head imaging without accurate information on
the head shape or the electrode positions. A library of fifty heads is employed
to build a principal component model for the typical variations in the shape of
the human head, which leads to a relatively accurate parametrization for head
shapes with only a few free parameters. The estimation of these shape
parameters and the electrode positions is incorporated in a regularized
Newton-type output least squares reconstruction algorithm. The presented
numerical experiments demonstrate that strong enough variations in the internal
conductivity of a human head can be detected by absolute electrical impedance
tomography even if the geometric information on the measurement configuration
is incomplete to an extent that is to be expected in practice.Comment: 25 pages, 12 figure
Neural networks for classification of strokes in electrical impedance tomography on a 3D head model
We consider the problem of the detection of brain hemorrhages from three
dimensional (3D) electrical impedance tomography (EIT) measurements. This is a
condition requiring urgent treatment for which EIT might provide a portable and
quick diagnosis. We employ two neural network architectures -- a fully
connected and a convolutional one -- for the classification of hemorrhagic and
ischemic strokes. The networks are trained on a dataset with samples
of synthetic electrode measurements generated with the complete electrode model
on realistic heads with a 3-layer structure. We consider changes in head
anatomy and layers, electrode position, measurement noise and conductivity
values. We then test the networks on several datasets of unseen EIT data, with
more complex stroke modeling (different shapes and volumes), higher levels of
noise and different amounts of electrode misplacement. On most test datasets we
achieve average accuracy with fully connected neural networks,
while the convolutional ones display an average accuracy . Despite
the use of simple neural network architectures, the results obtained are very
promising and motivate the applications of EIT-based classification methods on
real phantoms and ultimately on human patients.Comment: 17 pages, 11 figure
Computational framework for applying electrical impedance tomography to head imaging
This work introduces a computational framework for applying absolute electrical impedance tomography to head imaging without accurate information on the head shape or the electrode positions. A library of 50 heads is employed to build a principal component model for the typical variations in the shape of the human head, which leads to a relatively accurate parametrization for head shapes with only a few free parameters. The estimation of these shape parameters and the electrode positions is incorporated in a regularized Newton-type output least squares reconstruction algorithm. The presented numerical experiments demonstrate that strong enough variations in the internal conductivity of a human head can be detected by absolute electrical impedance tomography even if the geometric information on the measurement configuration is incomplete to an extent that is to be expected in practice
Computational framework for applying electrical impedance tomography to head imaging
This work introduces a computational framework for applying absolute electrical impedance tomography to head imaging without accurate information on the head shape or the electrode positions. A library of 50 heads is employed to build a principal component model for the typical variations in the shape of the human head, which leads to a relatively accurate parametrization for head shapes with only a few free parameters. The estimation of these shape parameters and the electrode positions is incorporated in a regularized Newton-type output least squares reconstruction algorithm. The presented numerical experiments demonstrate that strong enough variations in the internal conductivity of a human head can be detected by absolute electrical impedance tomography even if the geometric information on the measurement configuration is incomplete to an extent that is to be expected in practice.Peer reviewe
MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications
Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
Generalized averaged Gaussian quadrature and applications
A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal