Author
Adler A. Guardo R.
Institution
Montreal Univ., Que., Canada.
Title
A neural network image reconstruction technique for electrical impedance
tomography.
Source
IEEE Transactions on Medical Imaging, vol.13, no.4, Dec. 1994, pp.594-600.
Abstract
Reconstruction of images in electrical impedance tomography requires the
solution of a nonlinear inverse problem on noisy data. This problem is
typically ill-conditioned and requires either simplifying assumptions or
regularization based on a priori knowledge. The authors present a
reconstruction algorithm using neural network techniques which calculates
a linear approximation of the inverse problem directly from finite element
simulations of the forward problem. This inverse is adapted to the
geometry of the medium and the signal-to-noise ratio (SNR) used during
network training. Results show good conductivity reconstruction where
measurement SNR is similar to the training conditions. The advantages of
this method are its conceptual simplicity and ease of implementation, and
the ability to control the compromise between the noise performance and
resolution of the image reconstruction.