Microwave Technology for Brain Imaging and Monitoring: Physical Foundations, Potential and Limitations

  • Rosa Scapaticci
  • Mina Bjelogrlic
  • Jorge A. Tobon Vasquez
  • Francesca Vipiana
  • Michael Mattes
  • Lorenzo Crocco
Chapter

Abstract

This chapter provides an introduction to the physical principles underlying the adoption of microwave technology as a biomedical imaging modality for diagnosis and follow-up of neurological diseases and injuries (e.g., stroke, haematoma). In particular, a theoretical analysis, supported by numerical simulations and experiments, will be given to describe the physical constraints that arise in this kind of application and the relevant limitations. In addition, we discuss the main aspects to be faced when implementing microwave imaging technology in a clinical scenario, by exploiting a design procedure to determine the number of antennas needed to capture, in a non-redundant way, the largest part of the available data.

Notes

Acknowledgements

This work has been developed in the framework of COST Action TD1301 Development of a European-based Collaborative Network to Accelerate Technological, Clinical and Commercialisation Progress in the Area of Medical Microwave Imaging. It has been partially supported by Hasler Foundation, Switzerland, under Contract No. 13075 High accuracy volume integral equation solver for MRI grids and by the Italian Ministry of University and Research (MIUR) under the program MiBraScan—Microwave Brain Scanner for Cerebrovascular Diseases Monitoring.

References

  1. 1.
    Dielectric properties of body tissues in the frequency range 10 Hz–100 GHz. http://niremf.ifac.cnr.it/tissprop/
  2. 2.
    Balanis, C.A.: Advanced Enginnering Electromagnetics. Wileys (1989)Google Scholar
  3. 3.
    Bertero, M., Boccacci, P.: Introduction to Inverse Problems in Imaging. Institute of Physics, Bristol, UK (1998)Google Scholar
  4. 4.
    Botha, M.M.: Solving the volume integral equations of electromagnetic scattering. J. Comput. Phys. 218(1), 141–158 (2006)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Bucci, O.M., Crocco, L., Scapaticci, R.: On the optimal measurement configuration for magnetic nanoparticles-enhanced breast cancer microwave imaging. IEEE Trans. Biomed. Eng. 62, 407–414 (2015)CrossRefGoogle Scholar
  6. 6.
    Bucci, O.M., Crocco, L., Scapaticci, R., Bellizzi, G.: On the design of phased arrays for medical applications. Proc. IEEE 104(3), 633–648 (2016).  https://doi.org/10.1109/JPROC.2015.2504266
  7. 7.
    Catapano, I., Donato, L.D., Crocco, L., Bucci, O., Morabito, A., Isernia, T., Massa, R.: On quantitative microwave tomography of female breast. Prog. Electromagn. Res. 97, 75–93 (2009)CrossRefGoogle Scholar
  8. 8.
    Cavallini, A., Micieli, G., Marcheselli, S., Quaglini, S.: Role of monitoring in management of acute ischemic stroke patients. Stroke 34, 2599–2603 (2003)CrossRefGoogle Scholar
  9. 9.
    Cayoren, M., Akduman, I.: Emerging Electromagnetic Technologies for brain diseases diagnostics, monitoring and therapy, chap. Springer, Microwave Imaging for Continuous Monitoring of Brain Stroke (2018)Google Scholar
  10. 10.
    Choi, S.T.: Minimal residual methods for complex symmetric, skew symmetric, and skew hermitian systems. arXiv:1304.6782v2 [cs.MS] (2014)
  11. 11.
    Choi, S.T., Page, C., Saunders, M.: MINRES-QLP: a Krylov subspace method for indefinite or singular symmetric systems. arXiv:1003.4042v3 [math.NA] (2015)
  12. 12.
    Christ, A., et al.: The virtual family—development of surface-based anatomical models of two adults and two children for dosimetric simulations. Phys. Med. Biol. 55(2), N23–38 (2010)CrossRefGoogle Scholar
  13. 13.
    Donnan, G.A., Fisher, M., Macleod, M., Davis, S.M.: Stroke. Lancet 371(9624), 1612–1623 (2008)CrossRefGoogle Scholar
  14. 14.
    Gabriel, S., Lau, R.W., Gabriel, C.: The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. Phys. Med. Biol. 41, 2271–2293 (1996)CrossRefGoogle Scholar
  15. 15.
    Gan, H., Chew, W.: A discrete BCG-FFT algorithm for solving 3D inhomogeneous scatterer problems. J. Electromagn. Waves Appl. 9(10), 1339–1357 (2012)Google Scholar
  16. 16.
    Geyik, C., Wei, F., Massey, J., Yilmaz, A.: FDTD versus AIM for bioelectromagnetic analysis. In: Antennas and Propagation Society International Symposium (APSURSI), pp. 1–2 (2012)Google Scholar
  17. 17.
    Gubbi, J., Rao, A.S., Fang, K., Yan, B., Palaniswami, M.: Motor recovery monitoring using acceleration measurements in post acute stroke patients. Biomed Eng. OnLine 12(33) (2013)Google Scholar
  18. 18.
    Hamidipour, A., Henriksson, T., Hopfer, M., Planas, R., Semenov, S.: Emerging Electromagnetic Technologies for brain diseases diagnostics, monitoring and therapy, chap. Progress Towards Clinical Application. Electromagnetic Tomography for Brain Imaging and Stroke Diagnostics. Springer (2018)Google Scholar
  19. 19.
    Joachimowicz, N., Conessa, C., Henriksson, T., Duchêne, B.: Breast phantoms for microwave imaging. IEEE Antennas Wirel. Propag. Lett. 13, 1333–1336 (2014).  https://doi.org/10.1109/LAWP.2014.2336373
  20. 20.
    Markkanen, J., Lu, C.C., Cao, X., Yla-Oijala, P.: Analysis of volume integral equation formulations for scattering by high-contrast penetrable objects. IEEE Trans. Antennas Propag. 60(5), 2367–2374 (2012)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Markkanen, J., Yla-Oijala, P., Sihvola, A.: Discretization of volume integral equation formulations for extremely anisotropic materials. IEEE Trans. Antennas Propag. 60(11), 5195–5202 (2012)MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Olesen, J., Gustavsson, A., Svensson, M., Jonsson, B., Wittchen, H.U.: CDBE2010 study group, European Brain Council. The economic cost of brain disorders in Europe. Eur. J. Neurol. 19(1), 155–162 (2012)CrossRefGoogle Scholar
  23. 23.
    Persson, M., Fhager, A., Trefna, H.D., Yu, Y., McKelvey, T., Pegenius, G., Karlsson, J.E., Elam, M.: Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible. IEEE Trans. Biomed. Eng. 61(11), 2806–2817 (2014)CrossRefGoogle Scholar
  24. 24.
    Polimeridis, A., Villena, J., Daniel, L., White, J.: Stable FFT-JVIE solvers for fast analysis of highly inhomogeneous dielectric objects. J. Comput. Phys. 269, 280–296 (2014)CrossRefMATHGoogle Scholar
  25. 25.
    Rocco, A., Pasquini, M., Cecconi, E., Sirimarco, G., Ricciardi, M.C., Vicenzini, E., Altieri, M., Piero, V.D., Lenzi, G.L.: Monitoring after the acute stage of stroke: a prospective study. Stroke 38, 1225–1228 (2007)CrossRefGoogle Scholar
  26. 26.
    Rodrigues, D.B., Stauffer, P.R., Pereira, P.J.S., Maccarini, P.F.: Emerging Electromagnetic Technologies for brain diseases diagnostics, monitoring and therapy, chap. Microwave radiometry for noninvasive monitoring of brain temperature. Springer (2018)Google Scholar
  27. 27.
    Scapaticci, R., Donato, L.D., Catapano, I., Crocco, L.: A feasibility study on Microwave Imaging for brain stroke monitoring. Prog. Electromagn. Res. B 40, 305–324 (2012)CrossRefGoogle Scholar
  28. 28.
    Schwartz, J.T.: Nonlinear Functional Analysis. Gordon and Breach Science Publisher, New York (1987)Google Scholar
  29. 29.
    Shore, R.A.: Scattering of an electromagnetic linearly polarized plane wave by a multilayered sphere. IEEE Antennas Propag. Mag. 69–116 (2015)Google Scholar
  30. 30.
    Slaney, M., Kak, A., Larsen, L.: Limitations of imaging with first-order diffraction tomography. IEEE Trans. Microw. Theory Tech. 32, 860–874 (2009)CrossRefGoogle Scholar
  31. 31.
    Yaghjian, A.: Electric dyadic Green’s functions in the source region. Proc. IEEE 68(2), 248–263 (1980)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Rosa Scapaticci
    • 1
  • Mina Bjelogrlic
    • 2
  • Jorge A. Tobon Vasquez
    • 3
  • Francesca Vipiana
    • 3
  • Michael Mattes
    • 4
  • Lorenzo Crocco
    • 1
  1. 1.IREA-CNR, Institute for Electromagnetic Sensing of the Environment, National Research Council of ItalyNaplesItaly
  2. 2.Ecole Polytechnique Fédérale de LausanneLausanneSwitzerland
  3. 3.Turin PolytechnicTorinoItaly
  4. 4.Technical University of DenmarkKongens LyngbyDenmark

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