Enhancements in Electrical Impedance Tomography (EIT) image reconstruction for three-dimensional lung imaging

Electrical Impedance Tomography (EIT) is an imaging technique which calculates the electrical conductivity distribution within a medium from electrical measurements made at a series of electrodes on the medium surface. Reconstruction of conductivity or conductivity change images requires the solution of an ill-conditioned nonlinear inverse problem from noisy data. EIT is a hard problem as it is a particularly difficult example of attempting to recover a signal from noise.; To date most EIT scanners and algorithms have been designed for 2D applications. This simplifying assumption was originally used due to the prohibitive computational complexity of solving the larger 3D problem. Contemporary PC's can now calculate 3D solutions, however at the start of this thesis the prevailing algorithms in clinical use remain 2D models that rely on ad hoc tweaking to produce useful reconstructions.; The aim of this thesis is to develop enhancements in EIT image reconstruction for 3D lung imaging; to remove some of the limitations that continue to impede its routine use in the clinic. The aim is attained through the systematic achievement of the following four main objectives: (1) Improve the method of hyperparameter selection in order to eliminate case by case tweaking of parameters, provide repeatability of experiments, and reduce the number of reconstructions needed to find the best reconstruction for a given data set. (2) Increase the resolution of 3D models by increasing the number of elements in the Finite Element Model (FEM). This requires the development of an algorithm to solve the large inversion using readily available computers. (3) Determine the best way to collect 3D data from the chest given some equipment limitations and a specific set constraints concerning electrode placement. (4) Determine the viability of non-blurring regularization for 3D lung imaging.; The bulk of this thesis describes how the four objectives were successfully addressed with the result that some of the major limitations discouraging and preventing the routine use of 3D models for lung imaging have been eliminated. This thesis concludes with a recommendation for how to collect and reconstruct 3D EIT images of the lungs.