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Frequency-division multiplexing for electrical impedance tomography in biomedical applications.

Granot Y, Ivorra A, Rubinsky B - Int J Biomed Imaging (2007)

Bottom Line: This is achieved by injecting current through all of the current injecting electrodes simultaneously, and measuring all of the resulting voltages at once.Another significant issue arises when we are recording data in a dynamic environment where the properties change very fast.We discuss the FDM EIT method from the biomedical point of view and show results obtained with a simple experimental system.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Hebrew University of Jerusalem, 78b Ross Building, Jerusalem 91904, Israel.

ABSTRACT
Electrical impedance tomography (EIT) produces an image of the electrical impedance distribution of tissues in the body, using electrodes that are placed on the periphery of the imaged area. These electrodes inject currents and measure voltages and from these data, the impedance can be computed. Traditional EIT systems usually inject current patterns in a serial manner which means that the impedance is computed from data collected at slightly different times. It is usually also a time-consuming process. In this paper, we propose a method for collecting data concurrently from all of the current patterns in biomedical applications of EIT. This is achieved by injecting current through all of the current injecting electrodes simultaneously, and measuring all of the resulting voltages at once. The signals from various current injecting electrodes are separated by injecting different frequencies through each electrode. This is called frequency-division multiplexing (FDM). At the voltage measurement electrodes, the voltage related to each current injecting electrode is isolated by using Fourier decomposition. In biomedical applications, using different frequencies has important implications due to dispersions as the tissue's electrical properties change with frequency. Another significant issue arises when we are recording data in a dynamic environment where the properties change very fast. This method allows simultaneous measurements of all the current patterns, which may be important in applications where the tissue changes occur in the same time scale as the measurement. We discuss the FDM EIT method from the biomedical point of view and show results obtained with a simple experimental system.

No MeSH data available.


Typical example of a beta dispersion in a tissue. A specific band between 5 kHz and 20 kHz is shown in more detail to demonstrate the nearly linear relation.
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fig1: Typical example of a beta dispersion in a tissue. A specific band between 5 kHz and 20 kHz is shown in more detail to demonstrate the nearly linear relation.

Mentions: When working with tissues using multiple frequencies, one must consider the implications of the different dispersions. In Figure 1, we demonstrate a typical beta dispersion. The change in the tissue impedance is given as a function of frequency. We highlight a small section between 5 kHz and 20 kHz where our actual FDM EIT measurements were performed. Notice the considerable change in impedance between 100 kHz and 1 MHz. Since different electrodes use different frequencies, the associated impedance that is measured will vary as well. When the electrical properties of the different tissues that are measured are known or can be approximated, we are able to overcome this problem. Two possible solutions are either to choose a frequency band where the change is very small or to model this change and compensate for it when the data is processed. For example, in the frequency band between 5 kHz and 20 kHz, shown in Figure 1, the changes in conductivity are relatively small, so we can treat the conductivity in this region as constant. However, since we do have a realistic model for even that small change as a function of frequency, we can compensate for that function and reduce the errors that result from dispersion. This is similar to gauging the system for a specific tissue response. In the results section, we present an analysis of the errors that arise if these changes are not accounted for.


Frequency-division multiplexing for electrical impedance tomography in biomedical applications.

Granot Y, Ivorra A, Rubinsky B - Int J Biomed Imaging (2007)

Typical example of a beta dispersion in a tissue. A specific band between 5 kHz and 20 kHz is shown in more detail to demonstrate the nearly linear relation.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2211417&req=5

fig1: Typical example of a beta dispersion in a tissue. A specific band between 5 kHz and 20 kHz is shown in more detail to demonstrate the nearly linear relation.
Mentions: When working with tissues using multiple frequencies, one must consider the implications of the different dispersions. In Figure 1, we demonstrate a typical beta dispersion. The change in the tissue impedance is given as a function of frequency. We highlight a small section between 5 kHz and 20 kHz where our actual FDM EIT measurements were performed. Notice the considerable change in impedance between 100 kHz and 1 MHz. Since different electrodes use different frequencies, the associated impedance that is measured will vary as well. When the electrical properties of the different tissues that are measured are known or can be approximated, we are able to overcome this problem. Two possible solutions are either to choose a frequency band where the change is very small or to model this change and compensate for it when the data is processed. For example, in the frequency band between 5 kHz and 20 kHz, shown in Figure 1, the changes in conductivity are relatively small, so we can treat the conductivity in this region as constant. However, since we do have a realistic model for even that small change as a function of frequency, we can compensate for that function and reduce the errors that result from dispersion. This is similar to gauging the system for a specific tissue response. In the results section, we present an analysis of the errors that arise if these changes are not accounted for.

Bottom Line: This is achieved by injecting current through all of the current injecting electrodes simultaneously, and measuring all of the resulting voltages at once.Another significant issue arises when we are recording data in a dynamic environment where the properties change very fast.We discuss the FDM EIT method from the biomedical point of view and show results obtained with a simple experimental system.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science and Engineering, Hebrew University of Jerusalem, 78b Ross Building, Jerusalem 91904, Israel.

ABSTRACT
Electrical impedance tomography (EIT) produces an image of the electrical impedance distribution of tissues in the body, using electrodes that are placed on the periphery of the imaged area. These electrodes inject currents and measure voltages and from these data, the impedance can be computed. Traditional EIT systems usually inject current patterns in a serial manner which means that the impedance is computed from data collected at slightly different times. It is usually also a time-consuming process. In this paper, we propose a method for collecting data concurrently from all of the current patterns in biomedical applications of EIT. This is achieved by injecting current through all of the current injecting electrodes simultaneously, and measuring all of the resulting voltages at once. The signals from various current injecting electrodes are separated by injecting different frequencies through each electrode. This is called frequency-division multiplexing (FDM). At the voltage measurement electrodes, the voltage related to each current injecting electrode is isolated by using Fourier decomposition. In biomedical applications, using different frequencies has important implications due to dispersions as the tissue's electrical properties change with frequency. Another significant issue arises when we are recording data in a dynamic environment where the properties change very fast. This method allows simultaneous measurements of all the current patterns, which may be important in applications where the tissue changes occur in the same time scale as the measurement. We discuss the FDM EIT method from the biomedical point of view and show results obtained with a simple experimental system.

No MeSH data available.