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Terahertz dielectric spectroscopy of human brain gliomas and intact tissues ex vivo: double-Debye and double-overdamped-oscillator models of dielectric response.

A A GavdushN V ChernomyrdinG A KomandinIrina N DolganovaP V NikitinG R MusinaG M KatybaA S KucheryavenkoI V ReshetovA A PotapovValery Victorovich TuchinKirill I Zaytsev
Published in: Biomedical optics express (2020)
Terahertz (THz) technology offers novel opportunities in the intraoperative neurodiagnosis. Recently, the significant progress was achieved in the study of brain gliomas and intact tissues, highlighting a potential for THz technology in the intraoperative delineation of tumor margins. However, a lack of physical models describing the THz dielectric permittivity of healthy and pathological brain tissues restrains the further progress in this field. In the present work, the ex vivo THz dielectric response of human brain tissues was analyzed using relaxation models of complex dielectric permittivity. Dielectric response of tissues was parametrized by a pair of the Debye relaxators and a pair of the overdamped-oscillators - namely, the double-Debye (DD) and double-overdamped-oscillator (DO) models. Both models accurately reproduce the experimental curves for the intact tissues and the WHO Grades I-IV gliomas. While the DD model is more common for THz biophotonics, the DO model is more physically rigorous, since it satisfies the sum rule. In this way, the DO model and the sum rule were, then, applied to estimate the content of water in intact tissues and gliomas ex vivo. The observed results agreed well with the earlier-reported data, justifying water as a main endogenous label of brain tumors in the THz range. The developed models can be used to describe completely the THz-wave - human brain tissues interactions in the frameworks of classical electrodynamics, being quite important for further research and developments in THz neurodiagnosis of tumors.
Keyphrases
  • gene expression
  • high grade
  • physical activity
  • machine learning
  • single molecule
  • patients undergoing
  • brain injury
  • deep learning
  • cerebral ischemia