Low-Frequency Conductivity Tensor Imaging of the Human Head &lt;italic&gt;In Vivo&lt;/italic&gt; Using DT-MREIT: First Study

Chauhan, Munish; Indahlastari, Aprinda; Kasinadhuni, Aditya K.; Schär, Michael; Mareci, Thomas H.; Sadleir, Rosalind

doi:10.1109/tmi.2017.2783348

Cited by 47 publications

(46 citation statements)

References 53 publications

(80 reference statements)

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“…We systematically studied error in E-field simulations and determined solver parameters to sufficiently suppress numerical error. As TMS coil positioning systems, brain imaging techniques, and in-vivo electrical conductivity data are improved [58], lower numerical errors might become necessary.…”

Section: Discussionmentioning

confidence: 99%

Conditions for numerically accurate TMS electric field simulation

2020

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Background: Computational simulations of the E-field induced by transcranial magnetic stimulation (TMS) are increasingly used to understand its mechanisms and to inform its administration. However, characterization of the accuracy of the simulation methods and the factors that affect it is lacking. Objective: To ensure the accuracy of TMS E-field simulations, we systematically quantify their numerical error and provide guidelines for their setup. Method: We benchmark the accuracy of computational approaches that are commonly used for TMS Efield simulations, including the finite element method (FEM) with and without superconvergent patch recovery (SPR), boundary element method (BEM), finite difference method (FDM), and coil modeling methods. Results: To achieve cortical E-field error levels below 2%, the commonly used FDM and 1st order FEM require meshes with an average edge length below 0.4 mm, 1st order SPR-FEM requires edge lengths below 0.8 mm, and BEM and 2nd (or higher) order FEM require edge lengths below 2.9 mm. Coil models employing magnetic and current dipoles require at least 200 and 3000 dipoles, respectively. For thick solid-conductor coils and frequencies above 3 kHz, winding eddy currents may have to be modeled. Conclusion: BEM, FDM, and FEM all converge to the same solution. Compared to the common FDM and 1st order FEM approaches, BEM and 2nd (or higher) order FEM require significantly lower mesh densities to achieve the same error level. In some cases, coil winding eddy-currents must be modeled. Both electric current dipole and magnetic dipole models of the coil current can be accurate with sufficiently fine discretization.

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Section: Discussionmentioning

confidence: 99%

Conditions for numerically accurate TMS electric field simulation

2020

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“…In vivo n=1 adult healthy 0.774 ± 0.01 (Lee et al, 2015) MREIT In vivo n=2 adult healthy 0.787 (Ropella and Noll, 2017) MREIT in vivo n=4 adult healthy 0.028 ± 0.052 (Dabek et al, 2016) EIT in vivo 2 n=9 (4m) 32.5±10 healthy 0.627 ±0.037 (Akhtari et al, 2016) DAC in vitro 10 n=24 paediatric epilepsy 0.698 ±0.212 (Acar et al, 2016) E/MEG in vivo 0 n=2 (m) 21.5±2.12 healthy 0.718 ±0.019 (Gurler and Ider, 2017) MREIT in vivo n=1 23 healthy 0.817 ±0.218 (Lee et al, 2016) MREIT in vivo n=1 adult healthy 0.561 ± 0.382 (Koessler et al, 2017) EIT in vivo 50 n=15 (10m) 38±10 epilepsy 0.643 ± 0.0478 (Huang et al, 2017) EIT (Michel et al, 2017) MREIT in vivo n=1 29 healthy 0.89 ± 0.028 (Tha et al, 2018) MREIT in vivo n=30 (14m) 50.7 ± 18.2 tumour 0.486 (Chauhan et al, 2018) DTI in vivo 10 n=2 (m) healthy 0.939…”

Section: Eitmentioning

confidence: 99%

Variation in reported human head tissue electrical conductivity values

McCann

Pisano

Beltrachini

2019

Preprint

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Electromagnetic source characterisation requires accurate volume conductor models representing head geometry and the electrical conductivity field. Head tissue conductivity is often assumed from previous literature, however, despite extensive research, measurements are inconsistent. A meta-analysis of reported human head electrical conductivity values was therefore conducted to determine significant variation and subsequent influential factors. Of 3,121 identified publications spanning three databases, 56 papers were included in data extraction. Conductivity values were categorised according to tissue type, and recorded alongside methodology, measurement condition, current frequency, tissue temperature, participant pathology and age. We found variation in electrical conductivity of the wholeskull, the spongiform layer of the skull, isotropic, perpendicularly-and parallelly-oriented white matter (WM) and the brain-to-skull-conductivity ratio (BSCR) could be significantly attributed to a combination of differences in methodology and demographics. Specifically, whole-skull conductivity varied significantly with the employed method, whilst the spongiform layer differed with measurement condition. Grey matter conductivity fluctuated according to method and pathology, whilst isotropic WM differed according to all of the independent variables. Finally, the BSCR significantly varied with participant's age. The reported results provided evidence for head conductivity inconsistencies being dependent on methodology and demographics, supporting the hypothesis that conductivity values should not be assumed from the literature.

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“…Another approach is the combination of MRI with surface potential measurements as performed in electrical impedance tomography (MR EIT) [15][16][17], which requires current injection from several directions. Conductivity tensors have been reconstructed in vivo using MR EIT in combination with diffusion tensor imaging [18].…”

Section: Introductionmentioning

confidence: 99%

Demonstration of full tensor current density imaging using ultra-low field MRI

Hömmen

Storm

Höfner

2019

Magnetic Resonance Imaging

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Direct imaging of impressed dc currents inside the head can provide valuable conductivity information, possibly improving electro-magnetic neuroimaging. Ultra-low field magnetic resonance imaging (ULF MRI) at μT Larmor fields can be utilized for current density imaging (CDI). Here, a measurable impact of the magnetic field B J , generated by the impressed current density J, on the MR signal is probed using specialized sequences. In contrast to high-field MRI, the full tensor of B J can be derived without rotation of the subject in the scanner, due to a larger flexibility in the sequence design.We present an ULF MRI setup based on a superconducting quantum interference device (SQUID), which is operating at a noise level of 380 aT Hz −1/2 and capable of switching all imaging fields within a pulse sequence. Thereby, the system enables zero-field encoding, where the full tensor of B J is probed in the absence of other magnetic fields. 3D CDI is demonstrated on phantoms with different geometries carrying currents of approximately 2 mA corresponding to current densities between 0.45 and 8 A/m 2 . By comparison to an in vivo acquired head image, we provide insights to necessary improvements in signal-to-noise ratio.

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Low-Frequency Conductivity Tensor Imaging of the Human Head <italic>In Vivo</italic> Using DT-MREIT: First Study

Cited by 47 publications

References 53 publications

Conditions for numerically accurate TMS electric field simulation

Conditions for numerically accurate TMS electric field simulation

Variation in reported human head tissue electrical conductivity values

Demonstration of full tensor current density imaging using ultra-low field MRI

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