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Biomedical subjects

U Baysal

Publications and source records attributed to U Baysal.

2 recordsLinked to original sources

Application of electrical impedance tomography in diagnosis of emphysema--a clinical study.

In this paper, electrical impedance tomography (EIT) ventilation images from a group of 12 patients (11 patients with emphysema and one patient with only chronic obstructive pulmonary disease (COPD) (chronic bronchitis) and a group of 15 normal subjects were acquired using a Sheffield mark 1 EIT system, at the levels of second, fourth and sixth intercostal spaces. Patients were diagnosed based on CT scans of the thorax, pulmonary function tests and posteroanterior x-ray graphs. One of the patients with emphysema has also a malignant lung tumour. Ventilation-related conductivity changes at total lung capacity (TLC) relative to residual volume were measured quantitatively in EIT images. These quantitative values demonstrate marked differences compared to those values obtained from the EIT images of 15 normal subjects. The EIT images of the patients were also compared with the CT images. In addition to the visual examination of the EIT images a statistical confidence test is applied to compare the images of the patients with the images of the normal subjects. Prior to statistical analysis all images are normalized with TLC to minimize the effect of mismatch between the TLC of different subjects. A normal mean image is created by averaging the normalized images from the normal subjects, at each intercostal space level. Than a 95% confidence interval is defined for each normal mean image. For each image of the patients, a confidence test image, which represents the deviations from the 95% confidence interval of the normal mean image, is created. The regions with emphysematous bulla and parencyhma are detectable in the confidence test images as regions of positive and negative deviations from the confidence interval of the normal mean, respectively. In the test images, it is possible to differentiate emphysematous parenchyma from emphysematous bulla, tumour structure, and COPD. However, the emphysematous bulla, the tumour structure, and COPD result in the same type of defect in the test images and are therefore indistinguishable from each other. In some case, off-plane contributions in the EIT images may result in underestimation of the defects. EIT may be a useful screening device in detecting emphysema rather than a diagnostic tool.

Adult

A method for comparative evaluation of EIT algorithms using a standard data set.

The point spread function (PSF) is the most widely used tool for quantifying the spatial resolution of imaging systems. However, prerequisites for the proper use of this tool are linearity and space invariance. Because EIT is non-linear it is only possible to compare different reconstruction algorithms using a standard data set. In this study, the FEM is used to generate simulation data, which are used to investigate the non-linear behaviour of EIT, the space dependence of its PSF and its capability of resolving nearby objects. It is found that for the case of iterative backprojection (IterB), the full width half maximum (FWHM) values of single-object perturbations for central, intermediate and peripheral high-contrast objects are 27%, 18% and 14% of the imaging region diameter respectively. For the method based on singular value decomposition of the Geselowitz lead sensitivity matrix (GS-SVD), the FWHM is not space dependent and is 12% of the imaging region diameter. Conclusions obtained using single-object PSF studies must also be checked with double-object or more complex perturbations because EIT is non-linear. For example, the GS-SVD method fails to detect two widely separated objects unless the truncation level of SVD is carefully adjusted. With more truncation, however, the resolution of the method is worsened. Based on these and similar observations a set of simulation data, which is proposed for comparative evaluation of different EIT algorithms, is specified and explained in the conclusion section.

Algorithms