Open Access

Investigation of physiological swelling on conductivity distribution in lower leg subcutaneous tissue by electrical impedance tomography

R. Ogawa
R. Ogawa
, 
M. R. Baidillah
M. R. Baidillah
, 
S. Akita
S. Akita
 and 
M. Takei
M. Takei
   | May 14, 2020
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Published Online: May 14, 2020
Page range: 19 - 25
Received: Feb 04, 2020
DOI: https://doi.org/10.2478/joeb-2020-0004
Keywords
© 2020 R. Ogawa et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

The FEM forward mesh based on an MRI image. White, red, and blue part indicate SAT layer, muscle, and bone, respectively.
The FEM forward mesh based on an MRI image. White, red, and blue part indicate SAT layer, muscle, and bone, respectively.

Fig. 2

Experimental setup.
Experimental setup.

Fig. 3

Schematic diagram of the experimental schedule. After the 3rd measurement (t = 75 min), the subject did exercise to promote varying water contents compared to the original physiological state at the beginning of the experiment.
Schematic diagram of the experimental schedule. After the 3rd measurement (t = 75 min), the subject did exercise to promote varying water contents compared to the original physiological state at the beginning of the experiment.

Fig. 4

(a) the normalized absolute conductivity distribution of the right lower leg (viewed from the top) reconstructed by absolute EIT at 1st measurement; These are used as inhomogeneous reference data in (b). Time series of reconstructed relative conductivity distribution using the image-based reference EIT (b) and conventional time difference EIT (c) at 2nd, 3rd, and 4th measurement, respectively.
(a) the normalized absolute conductivity distribution of the right lower leg (viewed from the top) reconstructed by absolute EIT at 1st measurement; These are used as inhomogeneous reference data in (b). Time series of reconstructed relative conductivity distribution using the image-based reference EIT (b) and conventional time difference EIT (c) at 2nd, 3rd, and 4th measurement, respectively.

Fig. 5

The normalized temporal variation over segmental conductivity σseg in the predicted subcutaneous layer (white part in Fig.1) and segmental extracellular water volume Vseg in the right leg from image-based reference EIT (IBR-EIT) and MFBIA data, respectively.
The normalized temporal variation over segmental conductivity σseg in the predicted subcutaneous layer (white part in Fig.1) and segmental extracellular water volume Vseg in the right leg from image-based reference EIT (IBR-EIT) and MFBIA data, respectively.

Fig. 6

The magnitude of impedance at 0 min and 60 min on Day 1.
The magnitude of impedance at 0 min and 60 min on Day 1.

Schematic flow of image-based reference EIT.

Step 1:
Defining the filtering method to eliminate the muscle’s conductivity distribution and unexpected noise background
Obtain: Equation (4)
Step 2:
Using voltage data t = 0 as an initial conductivity distribution, selecting the mesh of SAT on the forward problem mesh condition, calculating the fat weighted threshold value σ^fat¯+std(σ^fat) \overline {{{\hat \sigma}_{fat}}} + std\left({{{\hat \sigma}_{fat}}} \right)
Obtain: the fat weighted threshold value σ^fat¯+std(σ^fat)s \overline {{{\hat \sigma}_{fat}}} + std{\left({{{\hat \sigma}_{fat}}} \right)_s}
Step 3:
Applying the filter on equation (4) to experimental data
Obtain:σabs and σ
Step 4:
Using voltage data t = 30, 60, and 90 mins to evaluate the varying water content to obtain the conductivity distribution, applying equation (5)
Obtain: σdiff
eISSN:
1891-5469
Language:
English
Publication timeframe:
Volume Open
Journal Subjects:
Engineering, Bioengineering and Biomedical Engineering, Biomedical Electronics, Life Sciences, Biophysics, Medicine, Biomedical Engineering, Physics, Spectroscopy and Metrology
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