Advertisement

Epidermal EIT Electrode Arrays for Cardiopulmonary Application and Fatty Liver Infiltration

  • Yuan Luo
  • Parinaz Abiri
  • Chih-Chiang Chang
  • Y. C. Tai
  • Tzung K. HsiaiEmail author
Chapter
  • 35 Downloads

Abstract

This chapter provides a detailed discussion on the tremendously promising imaging modality, electrical impedance tomography (EIT), and its application in noninvasive sensing with epidermal electrode arrays. We start with a fundamental account on the physical principal of EIT based on Maxwell equation. We then present the general mathematical scheme and software implementation for solving the EIT inverse problem. The focus of this chapter is the clinical application of EIT, and two different topics, cardiopulmonary monitoring and fatty liver detection, are discussed in detail. For cardiopulmonary monitoring, we look at the most recent development of using EIT in a variety of situation: mechanical ventilation in ICU, pulmonary perfusion, acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and cystic fibrosis (CF). For fatty liver detection, we present an inspiring study for measurement of fat content at multiple scales, from benchtop verification, to animal study, and to human subject testing. We also share our opinions in future directions for these two applications, respectively.

Keywords

Electrical impedance tomography Inverse problem Cardiopulmonary monitoring Nonalcoholic fatty liver disease 

References

  1. 1.
    Holder, D. S. (2004). Electrical impedance tomography: Methods, history and applications. Bristol: Institute of Physics Publishing.Google Scholar
  2. 2.
    Brown, B. H., & Seagar, A. D. (1987). The Sheffield data collection system. Clinical Physics and Physiological Measurement., 8, 91.CrossRefGoogle Scholar
  3. 3.
    Bayford, R. H. (2006). Bioimpedance tomography (electrical impedance tomography). Annual Review of Biomedical Engineering, 8, 63–91.CrossRefGoogle Scholar
  4. 4.
    Brown, B. H. (1997). Impedance pneumography, WO. 1997020499 A1.Google Scholar
  5. 5.
    Wilkinson, J., & Thanawala, V. (2009). Thoracic impedance monitoring of respiratory rate during sedation–is it safe? Anaesthesia, 64, 455–456.CrossRefGoogle Scholar
  6. 6.
    Gabriel, S., Lau, R., & Gabriel, C. (1996). The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. Physics in Medicine & Biology, 41, 2251.CrossRefGoogle Scholar
  7. 7.
    Griffiths, D. J. (1999). Introduction to electrodynamics. 3rd ed, Upper Saddle River: Prentice Hall.Google Scholar
  8. 8.
    Calderón, A. P. (2006). On an inverse boundary value problem. Computational & Applied Mathematics., 25, 133–138.MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Kirsch, A. (1996). An introduction to the mathematical theory of inverse problems. New York: Springer.Google Scholar
  10. 10.
    Nachman, A. I. (1988). Reconstructions from boundary measurements. Annals of Mathematics., 128, 531–576.MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Luo, Y., Abiri, P., Zhang, S., Chang, C.-C., Kaboodrangi, A. H., Li, R., Sahib, A. K., Bui, A., Kumar, R., & Woo, M. (2018). Non-invasive electrical impedance tomography for multi-scale detection of liver fat content. Theranostics., 8, 1636.CrossRefGoogle Scholar
  12. 12.
    Borcea, L. (2002). Electrical impedance tomography. Inverse Problems, 18, R99.MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Adler, A., & Lionheart, W. R. (2006). Uses and abuses of EIDORS: An extensible software base for EIT. Physiological Measurement., 27, S25.CrossRefGoogle Scholar
  14. 14.
    Schöberl, J. (1997). NETGEN an advancing front 2D/3D-mesh generator based on abstract rules. Computing and Visualization in Science, 1, 41–52.zbMATHCrossRefGoogle Scholar
  15. 15.
    Lionheart, W. R. (2004). EIT reconstruction algorithms: Pitfalls, challenges and recent developments. Physiological Measurement., 25, 125.CrossRefGoogle Scholar
  16. 16.
    Gong, B., Krueger-Ziolek, S., Moeller, K., Schullcke, B., & Zhao, Z. (2015). Electrical impedance tomography: Functional lung imaging on its way to clinical practice? Expert Review of Respiratory Medicine., 9, 721–737.CrossRefGoogle Scholar
  17. 17.
    Lobo, B., Hermosa, C., Abella, A., & Gordo, F. (2018). Electrical impedance tomography. Annals of Translational Medicine., 6, 26.CrossRefGoogle Scholar
  18. 18.
    Kotre, C. (1997). Electrical impedance tomography. British Journal of Radiology., 70, S200–S205.CrossRefGoogle Scholar
  19. 19.
    Brown, B., Leathard, A., Lu, L., Wang, W., & Hampshire, A. (1995). Measured and expected Cole parameters from electrical impedance tomographic spectroscopy images of the human thorax. Physiological Measurement., 16, A57.CrossRefGoogle Scholar
  20. 20.
    Frerichs, I., Amato, M. B., Van Kaam, A. H., Tingay, D. G., Zhao, Z., Grychtol, B., Bodenstein, M., Gagnon, H., Böhm, S. H., & Teschner, E. (2017). Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: Consensus statement of the TRanslational EIT developmeNt stuDy group. Thorax, 72, 83–93.CrossRefGoogle Scholar
  21. 21.
    Network, A. R. D. S. (2000). Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. New England Journal of Medicine, 342, 1301–1308.CrossRefGoogle Scholar
  22. 22.
    Eronia, N., Mauri, T., Maffezzini, E., Gatti, S., Bronco, A., Alban, L., Binda, F., Sasso, T., Marenghi, C., & Grasselli, G. (2017). Bedside selection of positive end-expiratory pressure by electrical impedance tomography in hypoxemic patients: A feasibility study. Annals of Intensive Care, 7, 76.CrossRefGoogle Scholar
  23. 23.
    Karsten, J., Grusnick, C., Paarmann, H., Heringlake, M., & Heinze, H. (2015). Positive end-expiratory pressure titration at bedside using electrical impedance tomography in post-operative cardiac surgery patients. Acta Anaesthesiologica Scandinavica, 59, 723–732.CrossRefGoogle Scholar
  24. 24.
    Hinz, J., Moerer, O., Neumann, P., Dudykevych, T., Frerichs, I., Hellige, G., & Quintel, M. (2006). Regional pulmonary pressure volume curves in mechanically ventilated patients with acute respiratory failure measured by electrical impedance tomography. Acta Anaesthesiologica Scandinavica, 50, 331–339.CrossRefGoogle Scholar
  25. 25.
    Costa, E. L., Borges, J. B., Melo, A., Suarez-Sipmann, F., Toufen, C., Bohm, S. H., & Amato, M. B. (2009). Bedside estimation of recruitable alveolar collapse and hyperdistension by electrical impedance tomography. Intensive Care Medicine, 35, 1132–1137.CrossRefGoogle Scholar
  26. 26.
    Wolf, G. K., Gómez-Laberge, C., Rettig, J. S., Vargas, S. O., Smallwood, C. D., Prabhu, S. P., Vitali, S. H., Zurakowski, D., & Arnold, J. H. (2013). Mechanical ventilation guided by electrical impedance tomography in experimental acute lung injury. Critical Care Medicine, 41, 1296–1304.CrossRefGoogle Scholar
  27. 27.
    Luepschen, H., Meier, T., Grossherr, M., Leibecke, T., Karsten, J., & Leonhardt, S. (2007). Protective ventilation using electrical impedance tomography. Physiological Measurement, 28, S247.CrossRefGoogle Scholar
  28. 28.
    Putensen, C., Wrigge, H., & Zinserling, J. (2007). Electrical impedance tomography guided ventilation therapy. Current Opinion in Critical Care, 13, 344–350.CrossRefGoogle Scholar
  29. 29.
    Lowhagen, K., Lindgren, S., Odenstedt, H., Stenqvist, O., & Lundin, S. (2011). A new non-radiological method to assess potential lung recruitability: A pilot study in ALI patients. Acta Anaesthesiologica Scandinavica, 55, 165–174.CrossRefGoogle Scholar
  30. 30.
    Odenstedt, H., Lindgren, S., Olegård, C., Erlandsson, K., Lethvall, S., Åneman, A., Stenqvist, O., & Lundin, S. (2005). Slow moderate pressure recruitment maneuver minimizes negative circulatory and lung mechanic side effects: Evaluation of recruitment maneuvers using electric impedance tomography. Intensive Care Medicine, 31, 1706–1714.CrossRefGoogle Scholar
  31. 31.
    Eyüboğlu, B. M., Brown, B. H., & Barber, D. C. (1989). In vivo imaging of cardiac related impedance changes. IEEE EMB Magazine, 8, 39–45.Google Scholar
  32. 32.
    Borges, J. B., Suarez-Sipmann, F., Bohm, S. H., Tusman, G., Melo, A., Maripuu, E., Sandström, M., Park, M., Costa, E. L., & Hedenstierna, G. (2011). Regional lung perfusion estimated by electrical impedance tomography in a piglet model of lung collapse. Journal of Applied Physiology, 112, 225–236.CrossRefGoogle Scholar
  33. 33.
    Force, A. D. T., Ranieri, V., & Rubenfeld, G. (2012). Acute respiratory distress syndrome. Journal of the American Medical Association, 307, 2526–2533.Google Scholar
  34. 34.
    Gattinoni, L., Pesenti, A., Avalli, L., Rossi, F., & Bombino, M. (1987). Pressure-volume curve of total respiratory system in acute respiratory failure: Computed tomographic scan study. American Review of Respiratory Disease, 136, 730–736.CrossRefGoogle Scholar
  35. 35.
    Franchineau, G., Bréchot, N., Lebreton, G., Hekimian, G., Nieszkowska, A., Trouillet, J.-L., Leprince, P., Chastre, J., Luyt, C.-E., & Combes, A. (2017). Bedside contribution of electrical impedance tomography to setting positive end-expiratory pressure for extracorporeal membrane oxygenation–treated patients with severe acute respiratory distress syndrome. American Journal of Respiratory and Critical Care Medicine, 196, 447–457.CrossRefGoogle Scholar
  36. 36.
    Gershon, A., Hwee, J., Victor, J. C., Wilton, A., Wu, R., Day, A., & T. To. (2015). Mortality trends in women and men with COPD in Ontario, Canada, 1996–2012. Thorax, 70, 121–126.CrossRefGoogle Scholar
  37. 37.
    Noordegraaf, A. V., Kunst, P. W., Janse, A., Marcus, J. T., Postmus, P. E., Faes, T. J., & de Vries, P. M. (1998). Pulmonary perfusion measured by means of electrical impedance tomography. Physiological Measurement, 19, 263.CrossRefGoogle Scholar
  38. 38.
    Vogt, B., Zhao, Z., Zabel, P., Weiler, N., & Frerichs, I. (2016). Regional lung response to bronchodilator reversibility testing determined by electrical impedance tomography in chronic obstructive pulmonary disease. American Journal of Physiology-Lung Cellular and Molecular Physiology, 311, L8–L19.CrossRefGoogle Scholar
  39. 39.
    Zhao, Z., Müller-Lisse, U., Frerichs, I., Fischer, R., & Möller, K. (2013). Regional airway obstruction in cystic fibrosis determined by electrical impedance tomography in comparison with high resolution CT. Physiological Measurement, 34, N107.CrossRefGoogle Scholar
  40. 40.
    Ogden, C. L., Carroll, M. D., Fryar, C. D., & Flegal, K. M. (2015). Prevalence of obesity among adults and youth: United States, 2011–2014: US Department of Health and Human Services, centers for disease control and ….Google Scholar
  41. 41.
    Fabbrini, E., Sullivan, S., & Klein, S. (2010). Obesity and nonalcoholic fatty liver disease: Biochemical, metabolic, and clinical implications. Hepatology, 51, 679–689.CrossRefGoogle Scholar
  42. 42.
    Marchesini, G., Bugianesi, E., Forlani, G., Cerrelli, F., Lenzi, M., Manini, R., Natale, S., Vanni, E., Villanova, N., & Melchionda, N. (2003). Nonalcoholic fatty liver, steatohepatitis, and the metabolic syndrome. Hepatology, 37, 917–923.CrossRefGoogle Scholar
  43. 43.
    Pagadala, M. R., & McCullough, A. J. (2012). Non-alcoholic fatty liver disease and obesity: Not all about body mass index. The American Journal of Gastroenterology, 107(12), 1859–1861. ed: Nature Publishing Group.CrossRefGoogle Scholar
  44. 44.
    Mishra, P., & Younossi, Z. M. (2007). Abdominal ultrasound for diagnosis of nonalcoholic fatty liver disease (NAFLD). The American Journal of Gastroenterology, 102, 2716.CrossRefGoogle Scholar
  45. 45.
    Chen, J., Talwalkar, J. A., Yin, M., Glaser, K. J., Sanderson, S. O., & Ehman, R. L. (2011). Early detection of nonalcoholic steatohepatitis in patients with nonalcoholic fatty liver disease by using MR elastography. Radiology, 259, 749–756.CrossRefGoogle Scholar
  46. 46.
    Hasgall, P., Di Gennaro, F., Baumgartner, C., Neufeld, E., Gosselin, M., Payne, D., Klingenböck, A., & Kuster, N. (2015). IT’IS database for thermal and electromagnetic parameters of biological tissues, Version 3.0, September 1st; 2015, ed.Google Scholar
  47. 47.
    Leporq, B., Ratiney, H., Pilleul, F., & Beuf, O. (2013). Liver fat volume fraction quantification with fat and water T1 and T2∗ estimation and accounting for NMR multiple components in patients with chronic liver disease at 1.5 and 3.0 T. European Radiology, 23, 2175–2186.CrossRefGoogle Scholar
  48. 48.
    Packard, R. R. S., Luo, Y., Abiri, P., Jen, N., Aksoy, O., Suh, W. M., Tai, Y.-C., & Hsiai, T. K. (2017). 3-D electrochemical impedance spectroscopy mapping of arteries to detect metabolically active but angiographically invisible atherosclerotic lesions. Theranostics, 7, 2431.CrossRefGoogle Scholar
  49. 49.
    Szumowski, J., Coshow, W., Li, F., Coombs, B., & Quinn, S. F. (1995). Double-echo three-point-dixon method for fat suppression MRI. Magnetic Resonance in Medicine, 34, 120–124.CrossRefGoogle Scholar
  50. 50.
    Ding, Y., Rao, S.-X., Chen, C.-Z., Li, R.-C., & Zeng, M.-S. (2015). Usefulness of two-point Dixon fat-water separation technique in gadoxetic acid-enhanced liver magnetic resonance imaging. World Journal of Gastroenterology: WJG, 21, 5017.CrossRefGoogle Scholar
  51. 51.
    Seo, J. K., & Woo, E. J. (2012). Nonlinear inverse problems in imaging. New York: Wiley.Google Scholar
  52. 52.
    Lee, J., Fei, P., Packard, R. R. S., Kang, H., Xu, H., Baek, K. I., Jen, N., Chen, J., Yen, H., & Kuo, C.-C. J. (2016). 4-dimensional light-sheet microscopy to elucidate shear stress modulation of cardiac trabeculation. The Journal of Clinical Investigation, 126, 1679–1690.CrossRefGoogle Scholar
  53. 53.
    Fei, P., Lee, J., Packard, R. R. S., Sereti, K.-I., Xu, H., Ma, J., Ding, Y., Kang, H., Chen, H., & Sung, K. (2016). Cardiac light-sheet fluorescent microscopy for multi-scale and rapid imaging of architecture and function. Scientific Reports, 6, 22489.CrossRefGoogle Scholar
  54. 54.
    Crabb, M., Davidson, J., Little, R., Wright, P., Morgan, A., Miller, C., Naish, J., Parker, G., Kikinis, R., & McCann, H. (2014). Mutual information as a measure of image quality for 3D dynamic lung imaging with EIT. Physiological Measurement, 35, 863.CrossRefGoogle Scholar
  55. 55.
    Ider, Y. Z., & Birgül, Ö. (2000). Use of the magnetic field generated by the internal distribution of injected currents for electrical impedance tomography (MR-EIT). Turkish Journal of Electrical Engineering & Computer Sciences, 6, 215–226.Google Scholar
  56. 56.
    Chen, M.-Y., Hu, G., He, W., Yang, Y.-L., & Zhai, J.-Q. (2010). A reconstruction method for electrical impedance tomography using particle swarm optimization. In Life system modeling and intelligent computing (pp. 342–350). Berlin, Heidelberg: Springer.Google Scholar
  57. 57.
    Feitosa, A. R., Ribeiro, R. R., Barbosa, V. A., de Souza, R. E., & dos Santos, W. P. (2014). Reconstruction of electrical impedance tomography images using particle swarm optimization, genetic algorithms and non-blind search. In Biosignals and Biorobotics Conference (2014): Biosignals and Robotics for Better and Safer Living (BRC), 5th ISSNIP-IEEE, 2014, pp. 1–6.Google Scholar
  58. 58.
    Martin, S. & Choi, C. T. (2015). Electrical impedance yomography: A reconstruction method based on neural networks and particle swarm optimization. In 1st Global Conference on Biomedical Engineering & 9th Asian-Pacific Conference on Medical and Biological Engineering, 2015, pp. 177–179.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuan Luo
    • 1
  • Parinaz Abiri
    • 2
  • Chih-Chiang Chang
    • 2
  • Y. C. Tai
    • 1
  • Tzung K. Hsiai
    • 1
    • 2
    Email author
  1. 1.Medical Engineering, California Institute of TechnologyPasadenaUSA
  2. 2.Department of BioengineeringUniversity of CaliforniaLos AngelesUSA

Personalised recommendations