| Abstract: | ABSTRACT: BACKGROUND: Electrical Impedance Tomography (EIT) is used as a fast clinical imaging technique formonitoring the health of the human organs such as lungs, heart, brain and breast. Eachpractical EIT reconstruction algorithm should be efficient enough in terms of convergencerate, and accuracy. The main objective of this study is to investigate the feasibility of preciseempirical conductivity imaging using a sinc-convolution algorithm in D-bar framework. METHODS: At the first step, synthetic and experimental data were used to compute an intermediate objectnamed scattering transform. Next, this object was used in a 2-day integral equation whichwas precisely and rapidly solved via sinc-convolution algorithm to find the square root of theconductivity for each pixel of image. For the purpose of comparison, multigrid and NOSERalgorithms were implemented under a similar setting. Quality of reconstructions of syntheticmodels was tested against GREIT approved quality measures. To validate the simulationresults, reconstructions of a phantom chest and a human lung were used. RESULTS: Evaluation of synthetic reconstructions shows that the quality of sinc-convolutionreconstructions is considerably better than that of each of its competitors in terms ofamplitude response, position error, ringing, resolution and shape-deformation. In addition, theresults confirm near-exponential and linear convergence rates for sinc-convolution andmultigrid, respectively. Moreover, the least degree of relative errors and the most degree oftruth were found in sinc-convolution reconstructions from experimental phantom data.Reconstructions of clinical lung data show that the related physiological effect is wellrecovered by sinc-convolution algorithm. CONCLUSIONS: Parametric evaluation demonstrates the efficiency of sinc-convolution to reconstruct accurateconductivity images from experimental data. Excellent results in phantom and clinicalreconstructions using sinc-convolution support parametric assessment results and suggest thesinc-convolution to be used for precise clinical EIT applications. |
