Automated gradient-based electrical properties tomography in the human brain using 7 Tesla MRI

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2 Scopus citations

Abstract

Electrical properties of the brain tissues may yield useful biomarkers for neurological disorders and diseases, as well as contribute to safety assurance of ultra-high-field MRI. It has been reported that using B1 maps from a multi-channel RF coil, the spatial variation of the electrical properties can be robustly retrieved. The absolute electrical property values can then be obtained by spatial integration, given that an integration seed point is assigned. In this study, we propose to exploit automatically detected seed points based on tissue piece-wise homogeneity (Helmholtz equation) for spatial integration. Numerical simulations of a numerical brain model and experiments involving 12 healthy volunteers were performed to demonstrate its feasibility and robustness in various noisy conditions and head positions. For in vivo imaging, we consistently observed higher conductivity and permittivity values in the white and gray matter compared to tabulated ex vivo probe measurement results found in the literature, a discrepancy that may be attributed to ex vivo experimental constraints. Our results suggest that the proposed technique produces consistent brain electrical properties in vivo that may contribute to improving diagnostic and therapeutic decisions.

Original languageEnglish (US)
Pages (from-to)258-266
Number of pages9
JournalMagnetic Resonance Imaging
Volume63
DOIs
StatePublished - Nov 2019

Bibliographical note

Funding Information:
This work was supported by NIH R21 EB017069 , R01 AT009263 , R01 EB021027 , R01 NS096761 , RF1 MH114233 , P30 NS076408 , P41EB015894 and S10RR026783 , and WM KECK Foundation. The authors thank Dr. Pramod Pisharaby and Dr. Catarina Saiote for assistance with T1w analysis, and Dr. Jiaen Liu for constructive discussions.

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • 7 T MRI
  • B mapping
  • Electrical properties
  • Human brain
  • Magnetic resonance based electrical properties tomography
  • Quantitative imaging

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