Different approaches for quantifying ventilation distribution and lung tissue properties by functional EIT

Abstract

We investigated five different methods which can be applied to quantitatively construct functional tomograms of the lungs. The focus was on the sensitivity of functional tomograms to errors in acquired data. To quantify this sensitivity, theoretical, error-free data sets of well-known properties were artificially generated based on a 'living thorax model'. Physiological time courses and a typical distribution of errors caused by a typical Goe-MF II EIT system were used for the calculations which encompassed a range up to 50 times greater than the initial error level (4 microV(rms max)-400 microV(rms max)). Additionally, low-pass filtering and principal component analysis (PCA) were used to quantify the effect of preprocessing the raw data. The results demonstrate that all methods based on fitting the local to the global time course were superior to the common functional tomograms utilizing standard deviation or maximum and minimum detection. Ventilation distribution was best quantified by the so-called VT methods. Filling capacity--a lung tissue property--was least dependent on increasing error levels. The errors introduced by filtering are significant with respect to a quantitative analysis of ventilation distribution. A preprocessing of raw data by applying a PCA performed well on the data sets which had been constructed but were, nonetheless, realistic. This approach appears to be highly promising for application on real data which is known to be erroneous.

Performance Evaluation of Five Types of Ag/AgCl Bio-Electrodes for Cerebral Electrical Impedance Tomography

“…1,2,6 To date, some research groups have already applied the technique into imaging study of thorax, abdomen, and other clinical trials. 13,14,17,24,39,44 In the aspect of cerebral EIT, the research mainly includes two directions: cerebral function imaging and dynamic monitoring of brain diseases. As to cerebral function imaging, the group directed by Holder systematically conducted a series of experiments to detect acute stroke, epileptic seizures, and visual evoked potentials by EIT method and had obtained some encouraging conclusions.…”

Research on Eit Boundary Measured Voltage Data Denoising Based on a Subspace Method

“…the data acquisition method is given as follows. Suppose that an opposite sinusoidal drive pattern and adjacent measurement method were applied, i.e., a current was applied on an electrode pair (1,9), and the voltage measured on (2,3), (3,4), (4,5), (5,6), (6,7), (7,8), (10,11), (11,12), (12,13), (13,14), (14,15), and (15,16) electrodes; we get 12 voltages. Similarly, a current should be applied on the pair (2, 10), etc.…”

“…hence, there is research especially devoted to the electrical impedance to the human body (6,14,15). in clinical applications (7), this can be used for early detection of breast cancer, real-time dynamic imaging, enables eit to perform bedside monitoring of thoracic and abdominal bleeding as well as of cerebral hemorrhage. Due to the advantages of being non-invasive and non-radiant, eit has great clinical value, especially to patients such as pregnant women.…”

Lung impedance measurements to monitor alveolar ventilation

“…If the local impedance change is plotted versus the total impedance change of the lung tissue covered by the electrode plane, this curve describes the filling characteristic. The slope of a straight line fit of this plot has been called filling capacity [3 ]. Filling capacity quantifies the local ability of lung tissue to change its volume related to total changes in volume.…”

“…Filling capacity quantifies the local ability of lung tissue to change its volume related to total changes in volume. This concept has been originally introduced by Kuhnel et al [60] to improve the image quality of EIT but also the physiological information seems to be of high value [3 ]. The basic concept of plotting the relationship between regional and total lung volume has been used by famous lung physiologists to characterize ventilation distribution [61,62].…”