Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.682783
Title: Electromagnetic induction tomography techniques for low conductivity biomedical application
Author: Ktistis, Christos
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2007
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Abstract:
This thesis considers the feasibility of using magnetic inductance tomography (MIT) to reconstruct images which represent the internal conductivity distribution of low contrast objects. The research focuses on a biomedical application, namely the measurement of human body composition. The thesis describes the development of a system which combines a photonic scanner for measuring the shape of the subject, with an experimental MIT system. The shape information can be used as a priori knowledge for the image reconstruction algorithm. The MIT system contained a full-scale 16 coil, circular sensor array, with 8 coils used for excitation and 8 for detection. The diameter of the object space was 75 cm and a commercial data acquisition system was used to interrogate the array. The measured data was reconstructed using linear algorithms and two kinds of sensitivity maps, one computed without shape information and the other with. The thesis contains results from a variety of tests illustrating the limits of each and the importance of knowing the external shape of the object. The shape scanning system is operating on the structured light principle. It consisted of four cameras and the 8 laser line generators which were integrated in the same scanning mechanisms used by the MIT system. Experiments were preformed in order to verify if the current design was capable to capture and reconstruct human body shape. Overall, the results of the research undertaken for this thesis support the feasibility of reconstructing internal features however more work is needed to obtain images of sufficient quality.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.682783  DOI: Not available
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