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Non-linear Electrical Tomography Reconstruction of Simple Test Objects and a Simulated Head Slice

Record type: Conference or Workshop Item (Other)

Fully non-linear reconstruction in Electrical Tomography produces images with well-defined characteristics when explicit guides are imposed on the accessible solutions. In this paper, we revisit the formulation of the problem and apply the algorithm to some simulated test objects, and to a simple 2-dimensional model of the human head. The results demonstrate the best fidelity of reconstruction which may be achieved with existing and potentially attainable levels of signal to noise. We use a finite element model with some adaptive capability so that the images generated by the chosen constraint are not perturbed by the coarseness of the mesh. The algorithm incorporates a number of optimisations to reduce the required computing power and storage space, these include: * Sparse matrix storage scheme and optimised sparse numerical handling * Problem-adapted element shape and density * Usage of high quality finite element meshes * Pre-evaluation of used quantities and matrices and application of numerical techniques such as the Woodbury formula.

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Citation

Molinari, M, Blott, BH, Cox, SJ and Daniell, GJ (2001) Non-linear Electrical Tomography Reconstruction of Simple Test Objects and a Simulated Head Slice At Proceedings of the 3rd EPSRC Engineering Network Meeting on Biomedical Applications of EIT. , p. 26.

More information

Published date: April 2001
Additional Information: Conference: 3rd EPSRC Engineering Network Meeting on Biomedical Applications of EIT, University College London, 4-6 April 2001 Organisation: EPSRC
Venue - Dates: Proceedings of the 3rd EPSRC Engineering Network Meeting on Biomedical Applications of EIT, 2001-04-01
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 255760
URI: http://eprints.soton.ac.uk/id/eprint/255760
PURE UUID: 03fd8dce-2e02-4637-a0ec-aa2d7927898d

Catalogue record

Date deposited: 26 Feb 2002
Last modified: 18 Jul 2017 09:51

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Contributors

Author: M Molinari
Author: BH Blott
Author: SJ Cox
Author: GJ Daniell

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