Conductivity Tensor Imaging of In Vivo Human Brain and Experimental Validation Using Giant Vesicle Suspension

Human brain mapping of low-frequency electrical conductivity tensors can realize patient-specific volume conductor models for neuroimaging and electrical stimulation. We report experimental validation and in vivo human experiments of a new electrodeless conductivity tensor imaging (CTI) method. From CTI imaging of a giant vesicle suspension using a 9.4-T MRI scanner, the relative error in the reconstructed conductivity tensor image was found to be less than 1.7% compared with the measured value using an impedance analyzer. In vivo human brain imaging experiments of five subjects were followed using a 3-T clinical MRI scanner. With the spatial resolution of 1.87 mm, the white matter conductivity showed considerably more position dependency compared with the gray matter and cerebrospinal fluid (CSF). The anisotropy ratio of the white matter was in the range of 1.96–3.25 with a mean value of 2.43, whereas that of the gray matter was in the range of 1.12–1.19 with a mean value of 1.16. The three diagonal components of the reconstructed conductivity tensors were from 0.08 to 0.27 S/m for the white matter, from 0.20 to 0.30 S/m for the gray matter, and from 1.55 to 1.82 S/m for the CSF. The reconstructed conductivity tensor images exhibited significant inter-subject variabilities in terms of frequency and position dependencies. The high-frequency and low-frequency conductivity values can quantify the total and extracellular water contents, respectively, at every pixel. Their difference can quantify the intracellular water content at every pixel. The CTI method can separately quantify the contributions of ion concentrations and mobility to the conductivity tensor.

[1]  C Gabriel,et al.  The dielectric properties of biological tissues: I. Literature survey. , 1996, Physics in medicine and biology.

[2]  A. Dale,et al.  Conductivity tensor mapping of the human brain using diffusion tensor MRI , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Rosalind J. Sadleir,et al.  Low-Frequency Conductivity Tensor Imaging of the Human Head In Vivo Using DT-MREIT: First Study , 2018, IEEE Transactions on Medical Imaging.

[4]  Eung Je Woo,et al.  Electrical Tissue Property Imaging at Low Frequency Using MREIT , 2014, IEEE Transactions on Biomedical Engineering.

[5]  Olaf Dössel,et al.  Determination of Electric Conductivity and Local SAR Via B1 Mapping , 2009, IEEE Transactions on Medical Imaging.

[6]  Saurav Z. K. Sajib,et al.  Extracellular Total Electrolyte Concentration Imaging for Electrical Brain Stimulation (EBS) , 2018, Scientific Reports.

[7]  Denis Le Bihan,et al.  Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.

[8]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[9]  Pabitra N. Sen,et al.  Time-dependent diffusion coefficient as a probe of geometry , 2004 .

[10]  R N Zare,et al.  Rapid preparation of giant unilamellar vesicles. , 1996, Proceedings of the National Academy of Sciences of the United States of America.

[11]  J. Gore,et al.  Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation. , 2014, Magnetic resonance imaging.

[12]  Tobias Voigt,et al.  Quantitative conductivity and permittivity imaging of the human brain using electric properties tomography , 2011, Magnetic resonance in medicine.

[13]  Eung Je Woo,et al.  Electrical tissue property imaging using MRI at dc and Larmor frequency , 2012 .

[14]  Eung Je Woo,et al.  Software Toolbox for Low-Frequency Conductivity and Current Density Imaging Using MRI , 2017, IEEE Transactions on Biomedical Engineering.

[15]  D. Djajaputra Electrical Impedance Tomography: Methods, History and Applications , 2005 .

[16]  Saurav Z. K. Sajib,et al.  Electrodeless conductivity tensor imaging (CTI) using MRI: basic theory and animal experiments , 2018, Biomedical Engineering Letters.

[17]  David R. Wozny,et al.  The electrical conductivity of human cerebrospinal fluid at body temperature , 1997, IEEE Transactions on Biomedical Engineering.

[18]  Sverre Grimnes,et al.  Bioimpedance and Bioelectricity Basics , 2000 .

[19]  Ohin Kwon,et al.  Anisotropic Conductivity Tensor Imaging of In Vivo Canine Brain Using DT-MREIT , 2017, IEEE Transactions on Medical Imaging.

[20]  Necip Gurler,et al.  Gradient‐based electrical conductivity imaging using MR phase , 2017, Magnetic resonance in medicine.

[21]  Ohin Kwon,et al.  Error Analysis of Nonconstant Admittivity for MR-Based Electric Property Imaging , 2012, IEEE Transactions on Medical Imaging.

[22]  Jin Keun Seo,et al.  Recent Progress and Future Challenges in MR Electric Properties Tomography , 2013, Comput. Math. Methods Medicine.

[23]  Eung Je Woo,et al.  Magnetic Resonance Electrical Impedance Tomography (MREIT) , 2011, SIAM Rev..

[24]  M. Hedehus,et al.  In vivo mapping of the fast and slow diffusion tensors in human brain , 2002, Magnetic resonance in medicine.

[25]  Steffen Leonhardt,et al.  Chest electrical impedance tomography examination, data analysis, terminology, clinical use and recommendations: consensus statement of the TRanslational EIT developmeNt stuDy group , 2016, Thorax.