Extracellular electrical conductivity property imaging by decomposition of high-frequency conductivity at Larmor-frequency using multi-b-value diffusion-weighted imaging

Lee, Mun Bae; Jahng, Geon‐Ho; Kim, Hyung Joong; Woo, Eung Je; Kwon, Oh In

doi:10.1371/journal.pone.0230903

Cited by 10 publications

(22 citation statements)

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“…In order to extend the electrical conductivity image to humans, it is necessary to overcome the limitations such as current injection, long acquisition time, and correlation with clinical evaluations. Fortunately, the recent conductivity tensor imaging (CTI) method, which provides not only information on conductivity without current injection, but also on the cellular environment, such as cell density and anisotropy, has been developed and various applications are being performed 16,40,41 . We expect that CTI with high sensitivity regarding tissue changes can provide information on liver tissues in situ that could be utilized for in vivo human imaging.…”

Section: Discussionmentioning

confidence: 99%

MR‐Based Electrical Conductivity Imaging of Liver Fibrosis in an Experimental Rat Model

Kim

Hur

et al. 2020

Magnetic Resonance Imaging

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BackgroundLiver fibrosis is characterized by the excessive accumulation of extracellular matrix proteins. Electrical conductivity imaging at low frequency can provide novel contrast because the contrast mechanisms originate from the changes in the concentration and mobility of ions in the extracellular space.PurposeTo evaluate the feasibility of an MR‐based electrical conductivity imaging that can detect the changes in a tissue condition associated with the progression of liver fibrosis.Study TypeProspective phantom and animal study.Animal ModelFibrosis was induced by weekly intraperitoneal injection of dimethylnitrosamine (DMN) in 45 male Sprague–Dawley rats.Field Strength/Sequence3T MRI with a multispin‐echo pulse sequence.AssessmentThe percentage change of conductivity (Δσ, %) in the same region‐of‐interest (ROI) was calculated from the DMN‐treated rats based on the values of the normal control rats. The percentage change was also calculated between the ROIs in each DMN‐treated group.Statistical TestsOne‐way analysis of variance (ANOVA) and a two‐sample t‐test were performed.ResultsLiver tissues in normal control rats showed a uniform conductivity distribution of 56.6 ± 4.4 (mS/m). In rats more than 5 weeks after induction, the fibrous region showed an increased conductivity of ≥12% compared to that of the corresponding normal control rats. From regional comparisons in the same liver, the fibrous region showed an increased conductivity of ≥11% compared to the opposite, less induced region of rats more than 5 weeks after induction. Liver samples from the fibrous region represent tissue damages such as diffuse centrilobular congestion with marked dilatation of central veins from the histological findings. Immunohistochemistry revealed significant levels of attenuated fibrosis and increased inflammatory response.Data ConclusionThe increased conductivity in the fibrous region is related to the changes of the extracellular space. The correlation between the collagen deposition and conductivity changes is essential for future clinical studies.Level of Evidence 2Technical Efficacy Stage 2J. MAGN. RESON. IMAGING 2021;53:554–563.

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Section: Discussionmentioning

confidence: 99%

MR‐Based Electrical Conductivity Imaging of Liver Fibrosis in an Experimental Rat Model

Kim

Hur

et al. 2020

Magnetic Resonance Imaging

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“…Equation 1assumes a linear relationship between the electrical conductivity tensor and the extracellular diffusion tensor. Different processing pipelines have been developed for the estimation of σ HF , χ e , d i , d e and D e (Sajib et al, 2018;Jahng et al, 2020;Lee et al, 2020).…”

Section: Overviewmentioning

confidence: 99%

“…The conductivity tensor imaging (CTI) method combines MR-EPT measurements with multi b-value DTI data, exploiting the correlation that exists between water diffusivity and electrical conductivity (Sen and Torquato, 1989;Tuch et al, 2001;Sajib et al, 2018). Since their introduction, CTI methods (Sajib et al, 2018;Jahng et al, 2020;Lee et al, 2020) enabled measuring the conductivity tensor at LF using conventional MR scanners, open source software (Sajib et al, 2017), and without requiring extra hardware.…”

Section: Introductionmentioning

confidence: 99%

“…Our methodology seeks to overcome the limitations of previous approaches, such as CTI mapping based on MR-EPT, by designing a framework independent from the Laplace operator. This objective is achieved by combining HF conductivity mapping based on water maps (Michel et al, 2017) and multi b-value DTI data (Tuch et al, 2001;Sajib et al, 2018;Jahng et al, 2020;Lee et al, 2020). Care is devoted toward a pipeline that optimizes (i) the acquisition of the DTI data, which is accelerated by means of multiband (Larkman et al, 2001) and SENSE (Pruessmann et al, 1999), (ii) the DTI pre-processing-for which a dedicated framework is employed to reduce noise and to correct for susceptibility induced spatial distortions and motion-and (iii) the fitting of a multi-compartment neurite orientation dispersion and density imaging (NODDI) (Zhang et al, 2012)-like model used to calculate the conductivity tensor (Lee et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques

et al. 2021

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Conductivity tensor imaging (CTI) has been recently proposed to map the conductivity tensor in 3D using magnetic resonance imaging (MRI) at the frequency range of the brain at rest, i.e., low-frequencies. Conventional CTI mapping methods process the trans-receiver phase of the MRI signal using the MR electric properties tomography (MR-EPT) technique, which in turn involves the application of the Laplace operator. This results in CTI maps with a low signal-to-noise ratio (SNR), artifacts at tissue boundaries and a limited spatial resolution. In order to improve on these aspects, a methodology independent from the MR-EPT method is proposed. This relies on the strong assumption for which electrical conductivity is univocally pre-determined by water concentration. In particular, CTI maps are calculated by combining high-frequency conductivity derived from water maps and multi b-value diffusion tensor imaging (DTI) data. Following the implementation of a pipeline to optimize the pre-processing of diffusion data and the fitting routine of a multi-compartment diffusivity model, reconstructed conductivity images were evaluated in terms of the achieved spatial resolution in five healthy subjects scanned at rest. We found that the pre-processing of diffusion data and the optimization of the fitting procedure improve the quality of conductivity maps. We achieve reproducible measurements across healthy participants and, in particular, we report conductivity values across subjects of 0.55 ± 0.01Sm, 0.3 ± 0.01Sm and 2.15 ± 0.02Sm for gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF), respectively. By attaining an actual spatial resolution of the conductivity tensor close to 1 mm in-plane isotropic, partial volume effects are reduced leading to good discrimination of tissues with similar conductivity values, such as GM and WM. The application of the proposed framework may contribute to a better definition of the head tissue compartments in electroencephalograpy/magnetoencephalography (EEG/MEG) source imaging and be used as biomarker for assessing conductivity changes in pathological conditions, such as stroke and brain tumors.

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“…Various exponential diffusion models have been proposed to describe signal attenuation with DWIs using more than two b-values [12,13,14,15,16,17]. Using the exponential diffusion models, an electrodeless method providing the low-frequency electrical property imaging without any external hardware was proposed [6,18,17].…”

Section: Introductionmentioning

confidence: 99%

Decomposition of High-Frequency Electrical Conductivity into Extracellular and Intracellular Compartments based on Two-Compartment Model using Low-to-High Multi-b Diffusion MRI

Lee

Kim

Kwon

2020

Preprint

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Background: As an object's electrical passive property, the electrical conductivity is proportional to the mobility and concentration of charged carriers that reflect the brain micro-structures. The measured Mb-DWI data by controlling the degree of applied diffusion weights can quantify the apparent mobility of water molecules within biological tissues. Without any external electrical stimulation, magnetic resonance electrical properties tomography (MREPT) techniques have successfully recovered the conductivity distribution at a Larmor-frequency. Methods: This work provides a non-invasive method to decompose the high-frequency conductivity into the extracellular medium conductivity based on a two-compartment model using multi-b diffusion-weighted imaging (Mb-DWI). To separate the intra- and extracellular micro-structures from the recovered high-frequency conductivity, we include higher b-values DWI and apply the random decision forests to stably determine the micro-structural diffusion parameters. Results: To demonstrate the proposed method, we conducted human experiments by comparing the results of reconstructed conductivity of extracellular medium and the conductivity in the intra-neurite and intra-cell body. Human experiments verify that the proposed method can recover the extracellular electrical properties from the high-frequency conductivity using a routine protocol sequence of MRI scan. Conclusion: We have proposed a method to decompose the electrical properties in the extracellular, intra-neurite, and soma compartments from the high-frequency conductivity map, reconstructed by solving the electro-magnetic equation with measured B1 phase signals.

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Extracellular electrical conductivity property imaging by decomposition of high-frequency conductivity at Larmor-frequency using multi-b-value diffusion-weighted imaging

Cited by 10 publications

References 32 publications

MR‐Based Electrical Conductivity Imaging of Liver Fibrosis in an Experimental Rat Model

MR‐Based Electrical Conductivity Imaging of Liver Fibrosis in an Experimental Rat Model

Conductivity Tensor Imaging of the Human Brain Using Water Mapping Techniques

Decomposition of High-Frequency Electrical Conductivity into Extracellular and Intracellular Compartments based on Two-Compartment Model using Low-to-High Multi-b Diffusion MRI

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