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We perform computer simulations of electrical impedance tomography to detect and localize the conductivity changes in brain. The reconstruction algorithm is derived from Newton's One Step Error Reconstructor (NOSER) and it minimizes algorithm execution time by pre-computing many quantities. It builds dynamic images which only display the conductivity changes in brain. We primarily presume that the head is a homogeneous unity according to NOSER algorithm. Then we find that the conductivity changes in brain are detected with a positional shift, which is mainly relative to the low conductivity of the skull. However, with a priori conductivity distribution information we assume that the conductivity distribution of the major head tissues is known and the pre-computed quantities are modified. Thus the conductivity changes in brain are detected with a relatively accurate localization. Future work in obtaining a more accurate a priori information of the head is expected to improve more on localization of the conductivity changes in brain. [ABSTRACT FROM AUTHOR]

Electrical impedance scanning (EIS) is a kind of potential bio-impedance measure technology, especially at aiding to diagnose the breast canner of women. The impedance of human tissue is comprised of not only resistance by also capacitance. By changing the frequency of the driving signal in turn, multi-frequency measurement results on the object are obtained. Multi-frequency data analysis is helpful to discover more information than single frequency data analysis. In this paper, we discuss two kinds of tissue circuit models, Cole-Cole model and three-element RC model. The multi-frequency data of EIS are measured using an EIS device designed by our research group. The frequency range is from 50Hz to 20 kHz. An operator presses the probe on the breast of the female subjects to measure data. The driving frequencies are changed in turn by the system software without operator's action. So, we can keep most of the conditions during the whole measurement. A group of 12 frequencies occupies less than 10 seconds. According to our measurement data, the real-part and imaginary-part curve of the admittance of measurement data is more adaptive to describe the EIS data than that of the impedance. Applying the least square method, we deduce the evaluated expression of all parameters of the two models in form of admittance. We compare real-imaginary curve of the admittance and the evaluated errors of the Cole-Cole model in form of admittance with those of the three-element model using the same a group multi-frequency data. The real-imaginary curve of the Cole-Cole model in form of admittance is more close the measurement data points than that of the three-element model. Moreover, the evaluated error of the former is less 10 times than that of the latter. Therefore, to EIS, the Cole-Cole model in form of admittance is a better multi-frequency data analysis model. [ABSTRACT FROM AUTHOR]

This paper presents a new electrical impedance tomography (EIT) software system for image monitoring of slow internal hemorrhage. The software is designed with object-oriented techniques and consists of several data acquisition modules, reconstruction algorithm modules and post-analysis modules. Lots of work has been done on the completion of the monitoring process, optimization of the data control and image visualization and user-friendly interface. Image monitoring experiments on both healthy human body for more than 24 hours and animal models were performed. The test results indicate that this software can meet the monitoring requirement. The software has provided a firm foundation for further researches on the improvement of reconstruction algorithm, data analysis and image processing, and has opened up a new prospect of long-term monitoring of slow impedance change. [ABSTRACT FROM AUTHOR]