Image reconstruction for Electrical Impedance Tomography (EIT) consists of two main stages: the forward modelling and the inverse solving. The physical model is discretized, typically using the Finite-Element Method (FEM) into tetrahedrals. Ideally, the physical model is discretized
as finely as possible for accurate representation. However, the EIT image reconstruction is an ill-posed, underdetermined problem, which makes this impractical This issue is prevalent when the model is a complex one, which is typically encountered in medical applications, whereby models may
consists of minute details that require a large amount of fine tetrahedrals to represent them, and these tetrahedrals do not feature into the region of interest, ultimately. This paper investigates methods to reduce the number of elements with the least compromise to the accuracy of the model.
Mathematical methods such as averaging and weighted averaging were employed in an effort to retain as much information from the detailed model as possible. Reconstructed images show that there is benefit in deploying these methods, in comparison with images whereby none of the prior knowledge
about the model was incorporated in the forward model. Results also show that images produced using the averaging method are superior to those that uses the weighted averaging method in ‘preserving’ the prior knowledge of the conductivity values of the imaged space.
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Electrical Impedance Tomography;
Finite Element Method;
Document Type: Research Article
Faculty of Engineering, Universiti Malaysia Sabah, Kota Kinabalu, Sabah, Malaysia
Department of Biomedical Imaging and University of Malaya Research Imaging Centre, University of Malaya, Kuala Lumpur, Malaysia
Publication date: November 1, 2017
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