The University of Southampton
University of Southampton Institutional Repository

Optimal imaging with adaptive mesh refinement in electrical tomography

Record type: Article

In non-linear electrical impedance tomography the goodness of fit of the trial images is assessed by the well-established statistical ?2 criterion applied to the measured and predicted datasets. Further selection from the range of images that fit the data is effected by imposing an explicit constraint on the form of the image, such as the minimization of the image gradients. In particular, the logarithm of the image gradients is chosen so that conductive and resistive deviations are treated in the same way. In this paper we introduce the idea of adaptive mesh refinement to the 2D problem so that the local scale of the mesh is always matched to the scale of the image structures. This improves the reconstruction resolution so that the image constraint adopted dominates and is not perturbed by the mesh discretization. The avoidance of unnecessary mesh elements optimizes the speed of reconstruction without degrading the resulting images. Starting with a mesh scale length of the order of the electrode separation it is shown that, for data obtained at presently achievable signal-to-noise ratios of 60 to 80 dB, one or two refinement stages are sufficient to generate high quality images.

PDF molinari_OptImAMR_PM23_2002_p121-128.pdf - Version of Record
Download (587kB)

Citation

Molinari, Marc, Blott, Barry H., Cox, Simon J. and Daniell, Geoffrey J. (2002) Optimal imaging with adaptive mesh refinement in electrical tomography Physiological Measurement, 23, (1), pp. 121-128. (doi:10.1088/0967-3334/23/1/311).

More information

Published date: February 2002
Keywords: electrical impedance tomography, optimal imaging, image smoothness constraint, adaptive mesh refinement, reconstruction algorithm

Identifiers

Local EPrints ID: 21939
URI: http://eprints.soton.ac.uk/id/eprint/21939
ISSN: 0967-3334
PURE UUID: 3b303876-e0e0-4d38-81c8-c37761d425fe

Catalogue record

Date deposited: 20 Mar 2006
Last modified: 17 Jul 2017 16:24

Export record

Altmetrics

Contributors

Author: Marc Molinari
Author: Barry H. Blott
Author: Simon J. Cox
Author: Geoffrey J. Daniell

University divisions


Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×