title: Optimal imaging with adaptive mesh refinement in electrical impedance tomography.
abstract: In non-linear electrical impedance tomography the goodness of fit of the trial images is assessed by the well-established statistical chi2 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.
authors.affiliation: Department of Electronics and Computer Science, University of Southampton, UK.
authors.forename: Marc
authors.initials: M
authors.lastname: Molinari
authors.forename: Barry H
authors.initials: BH
authors.lastname: Blott
authors.forename: Simon J
authors.initials: SJ
authors.lastname: Cox
authors.forename: Geoffrey J
authors.initials: GJ
authors.lastname: Daniell
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D017097:Electric Impedance; D004566:Electrodes; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D017711:Nonlinear Dynamics; D014054:Tomography / Q000706:statistics & numerical data*