Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.542005
Title: Integrating of geometric and finite element modelling of the thorax for EIT lung imaging
Author: Zifan, Ali
ISNI:       0000 0004 2709 4043
Awarding Body: City University
Current Institution: City, University of London
Date of Award: 2011
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Abstract:
In this thesis, for the first time, three-dimensional Electrical Impedance Tomography (EIT) conductivity images of the thorax, employing realistic Finite Element Models (FEM) of the chest-wall and lungs during an average respiratory cycle is presented. EIT is a non-invasive technique, which produces low-spatial and high-temporal resolution images of the internal impedance of the region of the body probed by the currents. One of the most promising clinical applications of EIT is real-time monitoring lung function in ambulatory or leu because of the rapid, non-invasive and non-ionizing nature of the measurements. We introduce a comprehensive framework aimed at creating a 3D dynamic respiratory finite element model of the thorax for EIT, in order to enhance the quality of EIT reconstructed conductivity images. Moreover, an automatic statistical segmentation method is also introduced in order to segment regions of conductivity change in reconstructed EIT lung image sequences. In order to achieve this, we first build a point distribution model (PDM) of the lungs and chest wall interaction from respiratory CT image sequences. Next, landmarks are automatically extracted and propagated to each example in the population using a multi-resolution diffeomorphic registration. The PDM allows the construction of an atlas of the average anatomy of the lungs and chest wall as well as their variability across the respiration cycle. Finally, once the model is built, the surface meshes are then tetrahedralized and after the subsequent attachment of electrodes, the EIT elliptical differential equation is solved on the volume meshes using finite elements in order to produce conductivity images of the thorax. Secondly, as mentioned earlier, we propose an alternative EIT segmentation framework, rather than the common manual approaches, which operates directly on the resulting FEM meshes, prior to any rasterisation on a rectangular gird in order to prevent the propagation of errors in the reconstructed resistivity regions, due to interpolation. Application of the latter method offers a much needed alternative to interactive segmentation currently favoured by EIT researchers and clinicians.
Supervisor: Not available Sponsor: Engineering and Physical Sciences Research Council
Qualification Name: Thesis (Ph.D) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.542005  DOI: Not available
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