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Microwave based detector for continuous assessment of intracerebral hemorrhage

  • YuHao Jiang
  • MinJi Zhao
  • Lu Wang
  • Li Yang
  • Yang Ju
Article
  • 15 Downloads

Abstract

Continuous detecting of the rate and size of hematoma expansion is crucial for intracerebral hemorrhage management and treatment. To continuously assess intracerebral hemorrhage in human head, a novel nondestructive microwave head detecting system is presented in this study. An open-ended cylindrical waveguide is employed as sensing antenna, which is operated associated with a coaxial cable for signal transmission and data acquisition. Measurement of amplitude data over the frequency range of 100–400 MHz is processed to evaluate the changes in intracerebral hemorrhage, based on the system’s operating principle that the sensor functions as a resonant cavity. Furthermore, 3D printed anatomically and dielectrically realistic human head phantoms with different lesions are fabricated to verify the efficacy of this proposed hemorrhagic stroke assessment system. It is worth noting that the quantitative results show that the system operating in TE111 mode is able to detect intracerebral hemorrhage size change as small as 1 cm3, demonstrating the possibility of this proposed head evaluating system in future preclinical trials.

Keywords

microwave evaluation intracerebral hemorrhage 

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References

  1. 1.
    Mackay J, Mensah G A, Mendis S, et al. The atlas of heart disease and stroke. World Health Organization, 2004Google Scholar
  2. 2.
    Qureshi A I, Mendelow A D, Hanley D F. Intracerebral haemorrhage. Lancet, 2009, 373: 1632–1644CrossRefGoogle Scholar
  3. 3.
    NINDS ICH Workshop Participants. Priorities for clinical research in intracerebral hemorrhage. Stroke, 2005, 36: 23CrossRefGoogle Scholar
  4. 4.
    Davis S M, Broderick J, Hennerici M, et al. Hematoma growth is a determinant of mortality and poor outcome after intracerebral hemorrhage. Neurology, 2006, 66: 1175–1181CrossRefGoogle Scholar
  5. 5.
    Goldstein J N, Fazen L E, Snider R, et al. Contrast extravasation on CT angiography predicts hematoma expansion in intracerebral hemorrhage. Neurology, 2007, 68: 889–894CrossRefGoogle Scholar
  6. 6.
    Robertson C S, Zager E L, Narayan R K, et al. Clinical evaluation of a portable near-infrared device for detection of traumatic intracranial hematomas. J Neurotrauma, 2010, 27: 1597–1604CrossRefGoogle Scholar
  7. 7.
    Xu C H, Wang L, Shi X T, et al. Real-time imaging and detection of intracranial haemorrhage by electrical impedance tomography in a piglet model. J Int Med Res, 2010, 38: 1596–1604CrossRefGoogle Scholar
  8. 8.
    Chen C, Fu F, Li B, et al. Experimental study of detection of brain tissue with electrical impedance tomography after cerebral ischemic. In: Long M, ed. World Congress on Medical Physics and Biomedical Engineering. Berlin, Heidelberg: Springer, 2013. 807–810Google Scholar
  9. 9.
    Nikolova N K. Microwave biomedical imaging. In: Webster J G, ed. Wiley Encyclopedia of Electrical and Electronics Engineering. Hoboken: John Wiley & Sons, 2014, 1–22CrossRefGoogle Scholar
  10. 10.
    Chandra R, Zhou H, Balasingham I, et al. On the opportunities and challenges in microwave medical sensing and imaging. IEEE Trans Biomed Eng, 2015, 62: 1667–1682CrossRefGoogle Scholar
  11. 11.
    Hadded W, Chang J, Rosenbury T, et al. Microwave hematoma detector for the rapid assessment of head injuries. Technical Report. Livermore: Lawrence Livermore National Lab., 2000CrossRefGoogle Scholar
  12. 12.
    Semenov S, Seiser B, Stoegmann E, et al. Electromagnetic tomography for brain imaging: From virtual to human brain. In: 2014 IEEE Conference on Antenna Measurements & Applications (CAMA). Antibes Juan-les-Pins: IEEE, 2014. 1–4Google Scholar
  13. 13.
    Bonazzoli M, Dolean V, Rapetti F, et al. Parallel preconditioners for high-order discretizations arising from full system modeling for brain microwave imaging. Int J Numer Model, 2018, 31: e2229CrossRefGoogle Scholar
  14. 14.
    Semenov S, Planas R, Hopfer M, et al. Electromagnetic tomography for brain imaging: Initial assessment for stroke detection. In: 2015 IEEE Biomedical Circuits and Systems Conference (BioCAS). Atlanta, 2015. 1–4Google Scholar
  15. 15.
    Mobashsher A T, Bialkowski K S, Abbosh A M, et al. Design and experimental evaluation of a non-invasive microwave head imaging system for intracranial haemorrhage detection. PLoS ONE, 2016, 11: e0152351CrossRefGoogle Scholar
  16. 16.
    Mobashsher A T, Mahmoud A, Abbosh A M. Portable wideband microwave imaging system for intracranial hemorrhage detection using improved back-projection algorithm with model of effective head permittivity. Sci Rep, 2016, 6: 20459CrossRefGoogle Scholar
  17. 17.
    Mobashsher A T, Abbosh A M. On-site rapid diagnosis of intracranial hematoma using portable multi-slice microwave imaging system. Sci Rep, 2016, 6: 37620CrossRefGoogle Scholar
  18. 18.
    Persson M, Fhager A, Trefná H D, et al. Microwave-based stroke diagnosis making global prehospital thrombolytic treatment possible. IEEE Trans Biomed Eng, 2014, 61: 2806–2817CrossRefGoogle Scholar
  19. 19.
    Ljungqvist J, Candefjord S, Persson M, et al. Clinical evaluation of a microwave-based device for detection of traumatic intracranial hemorrhage. J Neurotrauma, 2017, 34: 2176–2182CrossRefGoogle Scholar
  20. 20.
    Korfhagen J J, Kandadai M A, Clark J F, et al. A prototype device for non-invasive continuous monitoring of intracerebral hemorrhage. J Neurosci Methods, 2013, 213: 132–137CrossRefGoogle Scholar
  21. 21.
    Kandadai M A, Korfhagen J J, Beiler S, et al. In vivo testing of a noninvasive prototype device for the continuous monitoring of intracerebral hemorrhage. J Neurosci Methods, 2014, 235: 117–122CrossRefGoogle Scholar
  22. 22.
    Lagendijk J J W, Nilsson P. Hyperthermia dough: A fat and bone equivalent phantom to test microwave/radiofrequency hyperthermia heating systems. Phys Med Biol, 1985, 30: 709–712CrossRefGoogle Scholar
  23. 23.
    Gabriel C. Tissue equivalent material for hand phantoms. Phys Med Biol, 2007, 52: 4205–4210CrossRefGoogle Scholar
  24. 24.
    Jochmann T, Güllmar D, Haueisen J, et al. Influence of tissue conductivity changes on the EEG signal in the human brain—A simulation study. Z Med Phys, 2011, 21: 102–112CrossRefGoogle Scholar
  25. 25.
    Kandadai M A, Raymond J L, Shaw G J. Comparison of electrical conductivities of various brain phantom gels: Developing a ‘brain gel model’. Mater Sci Eng-C, 2012, 32: 2664–2667CrossRefGoogle Scholar
  26. 26.
    Mobashsher A T, Abbosh A M. Artificial human phantoms: Human proxy in testing microwave apparatuses that have electromagnetic interaction with the human body. IEEE Microwave, 2015, 16: 42–62CrossRefGoogle Scholar
  27. 27.
    Mobashsher A T, Abbosh A M. Three-dimensional human head phantom with realistic electrical properties and anatomy. Antennas Wirel Propag Lett, 2014, 13: 1401–1404CrossRefGoogle Scholar
  28. 28.
    Pozar D M. Microwave Engineering. New Jersey: John Wiley, 2005Google Scholar
  29. 29.
    Peyman A, Holden S J, Watts S, et al. Dielectric properties of porcine cerebrospinal tissues at microwave frequencies: In vivo, in vitro and systematic variation with age. Phys Med Biol, 2007, 52: 2229–2245CrossRefGoogle Scholar
  30. 30.
    Jiang Y, Zhao M, Wang H, et al. Non-invasive continuous monitoring of cerebral edema using portable microwave based system. IOP Conf Ser-Earth Environ Sci, 2018, 111: 012027CrossRefGoogle Scholar
  31. 31.
    González C A, Rubinsky B. The detection of brain oedema with frequency-dependent phase shift electromagnetic induction. Physiol Meas, 2006, 27: 539–552CrossRefGoogle Scholar
  32. 32.
    González C A, Rubinsky B. Frequency dependence of phase shift in edema: A theoretical study with magnetic induction. In: Engineering in Medicine and Biology Society. 27th Annual International Conference of the IEEE. Shanghai, 2006. 3518–3521Google Scholar
  33. 33.
    Bourqui J, Sill J M, Fear E C. A prototype system for measuring microwave frequency reflections from the breast. Int J Biomed Imag, 2012, 2012: 1–12Google Scholar
  34. 34.
    Meaney P M, Fanning M W, Raynolds T, et al. Initial clinical experience with microwave breast imaging in women with normal mammography. Academic Rad, 2007, 14: 207–218CrossRefGoogle Scholar
  35. 35.
    Wang X X, Li Z Y, Tian Y, et al. Two dimensional photoacoustic imaging using microfiber interferometric acoustic transducers. Opt Commun, 2018, 419: 41–46CrossRefGoogle Scholar
  36. 36.
    Fhager A, Gustafsson M, Nordebo S. Image reconstruction in microwave tomography using a dielectric Debye model. IEEE Trans Biomed Eng, 2012, 59: 156–166CrossRefGoogle Scholar

Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • YuHao Jiang
    • 1
  • MinJi Zhao
    • 2
  • Lu Wang
    • 1
  • Li Yang
    • 3
  • Yang Ju
    • 2
  1. 1.Medical Electronics and Information Technology Engineering Research CenterChongqing University of Posts and CommunicationsChongqingChina
  2. 2.Department of Mechanical Science and Engineering, Graduate School of EngineeringNagoya UniversityNagoyaJapan
  3. 3.Bioengineering CollegeChongqing UniversityChongqingChina

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