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The developed magnetic resonance electrical properties tomography (MREPT) techniques visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data. In biological tissues, electrical conductivity is influenced by ion concentrations and mobility. To visualize the anisotropic conductivity tensor of biological tissues, we use the Einstein-Smoluchowski equation, which links the diffusion coefficient to particle mobility. By assuming a correlation between ion mobility and water diffusivity, we aim to decompose the internal isotropic conductivity at Larmor frequency into anisotropic conductivity tensors within the intra- and extra-neurite compartments. The multi-compartment spherical mean technique (MC-SMT), utilizing both high and low b-value diffusion-weighted imaging (DWI) data, characterizes the diffusion of water molecules within and across the intra- and extra-neurite compartments by analyzing the microstructural intricacies and the foundational architectural arrangement of the brain's tissues. By analyzing the relationships between the measured DWI data, the microscopic diffusion signal, and the fiber orientation distribution function, we predict the DWI data for the intra- and extra-neurite compartments using spherical harmonic decomposition. Using the predicted DWI data for the intra- and extra-neurite compartments, we develop a conductivity tensor imaging method that operates specifically within the separated compartments. Human brain experiments, involving four healthy volunteers and a brain tumor patient, were performed to assess and confirm the reliability of the proposed method., Competing Interests: Declaration of competing interest The authors have no conflict of interest to report., (Copyright © 2024. Published by Elsevier Inc.)

Background: Fabry disease is an X-linked lysosomal storage disease caused by the impairment of α-galactosidase A. The complex intronic haplotype (CIH) variants, located in promoter and intronic regulatory lesions, has been found in patients with classical forms of Fabry disease. We present a case of Fabry disease in female monozygotic twins exhibiting the CIH mutation and classical manifestations., Case Presentation: A 61-year-old woman with a history of stroke, carotid artery occlusion, hypertrophic cardiomyopathy, and chronic kidney disease was referred to the nephrology clinic for management of her chronic kidney disease. Her monozygotic twin sister also presented with hypertrophic cardiomyopathy, atrial flutter, carotid stenosis, and proteinuria. Clinical symptoms and a comprehensive family history strongly suggested the presence of Fabry disease. Genetic analysis revealed the presence of 5 variants within a complex intronic haplotype (CIH): c.-10 C > T, c.369 + 990 C > A, c.370 - 81_370-77delCAGCC, c.640-16 A > G, and c.1000-22 C > T. We conducted a review of the patient's previous kidney biopsy findings, which demonstrated the presence of lamellated inclusion bodies in electron microscopy. Remarkably, both the monozygotic twin sister and her son exhibited the same genetic mutation. Enzyme replacement therapy was initiated for the patient. Her kidney function decreased throughout a thorough 2-year follow-up period, while there was a slight decrease in the left ventricular mass index., Conclusions: This is the first reported case of female monozygotic twins with the CIH variants representing cardiac, cerebrovascular, and renal manifestations suggestive of Fabry disease., (© 2024. The Author(s).)

Background: Although blood oxygen level-dependent (BOLD) functional MRI (fMRI) is a standard method, major BOLD signals primarily originate from intravascular sources. Magnetic resonance electrical properties tomography (MREPT)-based fMRI signals may provide additional insights into electrical activity caused by alterations in ion concentrations and mobilities., Purpose: This study aimed to investigate the neuronal response of conductivity during visual stimulation and compare it with BOLD., Materials and Methods: A total of 30 young, healthy volunteers participated in two independent experiments using BOLD and MREPT techniques with a visual stimulation paradigm at 3 T MRI. The first set of MREPT fMRI data was obtained using a multi-echo spin-echo (SE) echo planar imaging (EPI) sequence from 14 participants. The second set of MREPT fMRI data was collected from 16 participants using both a single-echo SE-EPI and a single-echo three-dimensional (3D) balanced fast-field-echo (bFFE) sequence. We reconstructed the time-course Larmor frequency conductivity to evaluate hemodynamics., Results: Conductivity values slightly increased during visual stimulation. Activation strengths were consistently stronger with BOLD than with conductivity for both SE-EPI MREPT and bFFE MREPT. Additionally, the activated areas were always larger with BOLD than MREPT. Some participants also exhibited decreased conductivity values during visual stimulations. In Experiment 1, conductivity showed significant differences between the fixation and visual stimulation blocks in the secondary visual cortex (SVC) and cuneus, with conductivity differences of 0.43 % and 0.47 %, respectively. No significant differences in conductivity were found in the cerebrospinal fluid (CSF) areas between the two blocks. In Experiment 2, significant conductivity differences were observed between the two blocks in the SVC, cuneus, and lingual gyrus for SE-EPI MREPT, with differences of 0.90 %, 0.67 %, and 0.24 %, respectively. Again, no significant differences were found in the CSF areas., Conclusion: Conductivity values increased slightly during visual stimulation in the visual cortex areas but were much weaker than BOLD responses. The conductivity change during visual stimulation was less than 1 % compared to the fixation block. No significant differences in conductivity were observed between the primary visual cortex (PVC)-CSF and SVC-CSF during fixation and visual stimulations, suggesting that the observed conductivity changes may not be related to CSF changes in the visual cortex but rather to diffusion changes. Future research should explore the potential of MREPT to detect neuronal electrical activity and hemodynamic changes, with further optimization of the MREPT technique., Competing Interests: Declaration of Competing Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The objectives of this study were to investigate how the extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer's disease (AD) patients compared with old cognitively normal (CN) people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between those conductivity values and cognitive decline. To do this, high-frequency conductivity (HFC) at the Larmor frequency was obtained using MRI-based electrical property tomography (MREPT) and was decomposed into EC and IC using information of multi-shell multi-gradient direction diffusion tensor images. This prospective single-center study included 20 patients with mild or moderate AD, 25 patients with amnestic MCI, and 21 old CN participants. After decomposing EC and IC from HFC for all participants, we performed voxel-based and regions-of-interest analyses to compare conductivity between the three participant groups and to evaluate the association with either age or the Mini-Mental State Examination (MMSE) scores. We found increased EC in AD compared to CN and MCI. EC was significantly negatively associated with MMSE scores in the insula, and middle temporal gyrus. EC might be used as an imaging biomarker for helping to monitor cognitive function., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Phlorotannins are natural polyphenolic compounds produced by brown marine algae and are currently found in nutritional supplements. Although they are known to cross the blood-brain barrier, their neuropharmacological actions remain unclear. Here we review the potential therapeutic benefits of phlorotannins in the treatment of neurodegenerative diseases. In mouse models of Alzheimer's disease, ethanol intoxication and fear stress, the phlorotannin monomer phloroglucinol and the compounds eckol, dieckol and phlorofucofuroeckol A have been shown to improve cognitive function. In a mouse model of Parkinson's disease, phloroglucinol treatment led to improved motor performance. Additional neurological benefits associated with phlorotannin intake have been demonstrated in stroke, sleep disorders, and pain response. These effects may stem from the inhibition of disease-inducing plaque synthesis and aggregation, suppression of microglial activation, modulation of pro-inflammatory signaling, reduction of glutamate-induced excitotoxicity, and scavenging of reactive oxygen species. Clinical trials of phlorotannins have not reported significant adverse effects, suggesting these compounds to be promising bioactive agents in the treatment of neurological diseases. We therefore propose a putative biophysical mechanism of phlorotannin action in addition to future directions for phlorotannin research., Competing Interests: YJK was previously employed, and OIK is currently employed by Botamedi Brain Health and Medical Care Company Limited. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kwon, Kwon, Hwang, Shin and Yang.)

The developed magnetic resonance electrical properties tomography (MREPT) can visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data from magnetic resonance imaging (MRI). The recovered high-frequency conductivity (HFC) value is highly complex and heterogeneous in a macroscopic imaging voxel. Using high and low b-value diffusion weighted imaging (DWI) data, the multi-compartment spherical mean technique (MC-SMT) characterizes the water molecule movement within and between intra- and extra-neurite compartments by analyzing the microstructures and underlying architectural organization of brain tissues. The proposed method decomposes the recovered HFC into the conductivity values in the intra- and extra-neurite compartments via the recovered intra-neurite volume fraction (IVF) and the diffusion patterns using DWI data. As a form of decomposition of intra- and extra-neurite compartments, the problem to determine the intra- and extra-neurite conductivity values from the HFC is still an underdetermined inverse problem. To solve the underdetermined problem, we use the compartmentalized IVF as a criterion to decompose the electrical properties because the ion-concentration and mobility have different characteristics in the intra- and extra-neurite compartments. The proposed method determines a representative apparent intra- and extra-neurite conductivity values by changing the underdetermined equation for a voxel into an over-determined minimization problem over a local window consisting of surrounding voxels. To suppress the noise amplification and estimate a feasible conductivity, we define a diffusion pattern distance to weight the over-determined system in the local window. To quantify the proposed method, we conducted a simulation experiment. The simulation experiments show the relationships between the noise reduction and the spatial resolution depending on the designed local window sizes and diffusion pattern distance. Human brain experiments (five young healthy volunteers and a patient with brain tumor) were conducted to evaluate and validate the reliability of the proposed method. To quantitatively compare the results with previously developed methods, we analyzed the errors for reconstructed extra-neurite conductivity using existing methods and indirectly verified the feasibility of the proposed method., (© 2023. The Author(s).)

Background: The previous studies reported increased concentrations of metallic ions, imbalanced Na+ and K+ ions, and the increased mobility of protons by microstructural disruptions in Alzheimer's disease (AD)., Purpose: (1) to apply a high-frequency conductivity (HFC) mapping technique using a clinical 3T MRI system, (2) compare HFC values in the brains of participants with AD, amnestic mild cognitive impairment (MCI), and cognitively normal (CN) elderly people, (3) evaluate the relationship between HFC values and cognitive decline, and (4) explore usefulness of HFC values as an imaging biomarker to evaluate the differentiation of AD from CN., Materials and Methods: This prospective study included 74 participants (23 AD patients, 27 amnestic MCI patients, and 24 CN elderly people) to explore the clinical application of HFC mapping in the brain from March 2019 to August 2021. We performed statistical analyses to compare HFC maps between the three participant groups, evaluate the association of HFC maps with Mini-Mental State Examination (MMSE) scores, and to evaluate the differentiation between the participant groups for HFC values for some brain areas., Results: We obtained a good HFC map non-invasively. The HFC value was higher in the AD group than in the CN and MCI groups. MMSE scores were negatively associated with HFC values. Age was positively associated with HFC values. The HFC value in the insula has a high area under the receiver operating characteristic (ROC) curve (AUC) value to differentiate AD patients from the CN participants (Sensitivity [ SE ] = 82, Specificity [ SP ] =97, AUC = 0.902, p < 0.0001), better than gray matter volume (GMV) in hippocampus ( SE = 79, SP = 83, AUC = 0.880, p < 0.0001). The classification for differentiating AD from CN was highest by adding the hippocampal GMV to the insular HFC value ( SE = 87, SP = 87, AUC = 0.928, p < 0.0001)., Conclusion: High-frequency conductivity values were significantly increased in the AD group compared to the CN group and increased with age and disease severity. HFC values of the insula along with the GMV of the hippocampus can be used as an imaging biomarker to improve the differentiation of AD from CN., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Park, Jung, Lee, Rhee, Ryu, Cho, Kwon and Jahng.)

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations., Competing Interests: The authors have declared that no competing interests exist.

Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field associated with the radiofrequency (RF) magnetic field, the high-frequency conductivity and permittivity distributions inside the human brain have been reconstructed based on the Maxwell's equation. Starting from the Maxwell's equation, the complex permittivity can be described as a second order elliptic partial differential equation. The established reconstruction algorithms have focused on simplifying and/or regularizing the elliptic partial differential equation to reduce the noise artifact. Using the nonlinear relationship between the Maxwell's equation, measured magnetic field, and conductivity distribution, we design a deep learning model to visualize the high-frequency conductivity in the brain, directly derived from measured magnetic flux density. The designed moving local window multi-layer perceptron (MLW-MLP) neural network by sliding local window consisting of neighboring voxels around each voxel predicts the high-frequency conductivity distribution in each local window. The designed MLW-MLP uses a family of multiple groups, consisting of the gradients and Laplacian of measured B1 phase data, as the input layer in a local window. The output layer of MLW-MLP returns the conductivity values in each local window. By taking a non-local mean filtering approach in the local window, we reconstruct a noise suppressed conductivity image while maintaining spatial resolution. To verify the proposed method, we used B1 phase datasets acquired from eight human subjects (five subjects for training procedure and three subjects for predicting the conductivity in the brain)., Competing Interests: The authors have declared that no competing interests exist.

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 multi-b diffusion-weighted imaging (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 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 phantom and human experiments by comparing the results of reconstructed conductivity of extracellular medium and the conductivity in the intra-neurite and intra-cell body. The phantom and 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.

Diffusion weighted imaging based on random Brownian motion of water molecules within a voxel provides information on the micro-structure of biological tissues through water molecule diffusivity. As the electrical conductivity is primarily determined by the concentration and mobility of ionic charge carriers, the macroscopic electrical conductivity of biological tissues is also related to the diffusion of electrical ions. This paper aims to investigate the low-frequency electrical conductivity by relying on a pre-defined biological model that separates the brain into the intracellular (restricted) and extracellular (hindered) compartments. The proposed method uses B1 mapping technique, which provides a high-frequency conductivity distribution at Larmor frequency, and the spherical mean technique, which directly estimates the microscopic tissue structure based on the water molecule diffusivity and neurite orientation distribution. The total extracellular ion concentration, which is separated from the high-frequency conductivity, is recovered using the estimated diffusivity parameters and volume fraction in each compartment. We propose a method to reconstruct the low-frequency dominant conductivity tensor by taking into consideration the extracted extracellular diffusion tensor map and the reconstructed electrical parameters. To demonstrate the reliability of the proposed method, we conducted two phantom experiments. The first one used a cylindrical acrylic cage filled with an agar in the background region and four anomalies for the effect of ion concentration on the electrical conductivity. The other experiment, in which the effect of ion mobility on the conductivity was verified, used cell-like materials with thin insulating membranes suspended in an electrolyte. Animal and human brain experiments were conducted to visualize the low-frequency dominant conductivity tensor images. The proposed method using a conventional MRI scanner can predict the internal current density map in the brain without directly injected external currents., (Copyright © 2020. Published by Elsevier Inc.)

Background: Electrical conductivity of a biological tissue at low frequencies can be approximately expressed as a tensor. Noting that cross-sectional imaging of a low-frequency conductivity tensor distribution inside the human body has wide clinical applications of many bioelectromagnetic phenomena, a new conductivity tensor imaging (CTI) technique has been lately developed using an MRI scanner. Since the technique is based on a few assumptions between mobility and diffusivity of ions and water molecules, experimental validations are needed before applying it to clinical studies., Methods: We designed two conductivity phantoms each with three compartments. The compartments were filled with electrolytes and/or giant vesicle suspensions. The giant vesicles were cell-like materials with thin insulating membranes. We controlled viscosity of the electrolytes and the giant vesicle suspensions to change ion mobility and therefore conductivity values. The conductivity values of the electrolytes and giant vesicle suspensions were measured using an impedance analyzer before CTI experiments. A 9.4-T research MRI scanner was used to reconstruct conductivity tensor images of the phantoms., Results: The CTI technique successfully reconstructed conductivity tensor images of the phantoms with a voxel size of [Formula: see text]. The relative [Formula: see text] errors between the conductivity values measured by the impedance analyzer and those reconstructed by the MRI scanner was between 1.1 and 11.5., Conclusions: The accuracy of the new CTI technique was estimated to be high enough for most clinical applications. Future studies of animal models and human subjects should be pursued to show the clinical efficacy of the CTI technique.

Magnetic resonance electrical properties tomography (MREPT) uses the B1 mapping technique to provide the high-frequency conductivity distribution at Larmor frequency that simultaneously reflects the intracellular and extracellular effects. In biological tissues, the electrical conductivity can be described as the concentration and mobility of charge carriers. For the water molecule diffusivity, diffusion weighted imaging (DWI) measures the random Brownian motion of water molecules within biological tissues. The DWI data can quantitatively access the mobility of microscopic water molecules within biological tissues. By measuring multi-b-value DWI data and the recovered high-frequency conductivity at Larmor frequency, we propose a new method to decompose the conductivity into the total ion concentration and mobility in the extracellular space (ECS) within a routinely applicable MR scan time. Using the measured multi-b-value DWI data, a constrained compartment model is designed to estimate the extracellular volume fraction and extracellular mean diffusivity. With the extracted extracellular volume fraction and water molecule diffusivity, we directly reconstruct the low-frequency electrical properties including the extracellular mean conductivity and extracellular conductivity tensor. To demonstrate the proposed method by comparing the ion concentration and the ion mobility, we conducted human experiments for the proposed low-frequency conductivity imaging. Human experiments verify that the proposed method can recover the low-frequency electrical properties using a conventional MRI scanner., Competing Interests: The authors have declared that no competing interests exist.

Radiation therapy (RT) has been widely used as a powerful treatment tool to address cancerous tissues because of its ability to control cell growth. Its ionizing radiation damages the DNA of cancerous tissues, leading to cell death. Medical imaging, however, still has limitations regarding the reliability of its assessment of tissue response and in predicting the treatment effect because of its inability to provide contrast information on the gradual, minute tissue changes after RT. A recently developed magnetic resonance (MR)-based conductivity imaging method may provide direct, highly sensitive information on this tissue response because its contrast mechanism is based on the concentration and mobility of ions in intracellular and extracellular spaces. In this feasibility study, we applied T2-weighted, diffusion-weighted, and electrical conductivity imaging to mouse brain, thus, using the MR imaging to map the tissue response after radiation exposure. To evaluate the degree of response, we measured the T2 relaxation, apparent diffusion coefficient (ADC), and electrical conductivity of brain tissues before and after irradiation. The conductivity images, which showed significantly higher sensitivity than other MR imaging methods, indicated that the contrast is distinguishable in different ways at different areas of the brain. Future studies will focus on verifying these results and the long-term evaluation of conductivity changes using various irradiation methods for clinical applications.

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.

Electrical brain stimulation (EBS) is a promising medical treatment method for brain neurological disorders through the direct or indirect excitation by injecting an electric current. At present, it is difficult to directly measure the distribution of the electric current delivered by electrodes inside tissues. By applying low-frequency ([Formula: see text]1 kHz) external electrical brain stimulation (EBS), the low-frequency conductivity around the cells is uneven due to asymmetric cellular structures. We propose a method of electrical property imaging using the measured one component magnetic flux density by EBS and diffusion tensor imaging (DTI). The low-frequency electrical anisotropic conductivity tensor can be decomposed into the ion concentration and the mobility tensor of charge carriers. By analyzing the role of the diffusion tensor, we reconstruct the apparent anisotropic tensor by EBS using the [Formula: see text]-component of measured magnetic flux density data and the estimated diffusion tensor. Using only the measured [Formula: see text]-component of magnetic flux density, the orthotropic conductivity tensor can be approximately recovered. The orthotropic conductivity tensor is not exact, but only reflects the extracellular space (ECS) effects. By comparing the components of orthotropic tensor and diffusion tensor, we stably determine a scale factor which primarily reflects the concentration of total ions in the extracellular space (ECS). Animal experiments verify that the proposed method recovers the anisotropic conductivity tensor which can visualize electrical properties during EBS of the brain. A direct reconstruction method for the apparent anisotropic conductivity tensor imaging during EBS was proposed to analyze unknown effects to brain tissue.

Anisotropic diffusion MRI techniques using single-shell or multi-shell acquisitions have been proposed as a means to overcome some limitations imposed by diffusion tensor imaging (DTI), especially in complex models of fibre orientation distribution in voxels. A long acquisition time for the angular resolution of diffusion MRI is a major obstacle to practical clinical implementations. In this paper, we propose a novel method to improve angular resolution of diffusion MRI acquisition using given diffusion gradient (DG) directions. First, we define a local diffusion pattern map of diffusion MR signals on a single shell in given DG directions. Using the local diffusion pattern map, we design a prediction scheme to determine the best DG direction to be synthesized within a nearest neighborhood DG directions group. Second, the local diffusion pattern map and the spherical distance on the shell are combined to determine a synthesized diffusion signal in the new DG direction. Using the synthesized and measured diffusion signals on a single sphere, we estimate a spin orientation distribution function (SDF) with human brain data. Although the proposed method is applied to SDF, a basic idea is to increase the angular resolution using the measured diffusion signals in various DG directions. The method can be applicable to different acquired multi-shell data or diffusion spectroscopic imaging (DSI) data. We validate the proposed method by comparing the recovered SDFs using the angular resolution enhanced diffusion signals with the recovered SDF using the measured diffusion data. The developed method provides an enhanced SDF resolution and improved multiple fiber structure by incorporating synthesized signals. The proposed method was also applied neurite orientation dispersion and density imaging (NODDI) using multi-shell acquisitions., (Copyright © 2018 Elsevier Inc. All rights reserved.)

In oriental medicine, curcumin is used to treat inflammatory diseases, and its anti-inflammatory effect has been reported in recent research. In this feasibility study, the hepatoprotective effect of curcumin was investigated using a rat liver cirrhosis model, which was induced with dimethylnitrosamine (DMN). Together with biochemical analysis, we used a magnetic resonance-based electrical conductivity imaging method to evaluate tissue conditions associated with a protective effect. The effects of curcumin treatment and lactulose treatment on liver cirrhosis were compared. Electrical conductivity images indicated that liver tissues damaged by DMN showed decreased conductivity compared with normal liver tissues. In contrast, cirrhotic liver tissues treated with curcumin or lactulose showed increased conductivity than tissues in the DMN-only group. Specifically, conductivity of cirrhotic liver after curcumin treatment was similar to that of normal liver tissues. Histological staining and immunohistochemical examination showed significant levels of attenuated fibrosis and decreased inflammatory response after both curcumin and lactulose treatments compared with damaged liver tissues by DMN. The conductivity imaging and biochemical examination results indicate that curcumin's anti-inflammatory effect can prevent the progression of irreversible liver dysfunction.

Transcranial direct current stimulation (tDCS) is a widely used non-invasive brain stimulation technique by applying low-frequency weak direct current via electrodes attached on the head. The tDCS using a fixed current between 1 and 2 mA has relied on computational modelings to achieve optimal stimulation effects. Recently, by measuring the tDCS current induced magnetic field using an MRI scanner, the internal current pathway has been successfully recovered. However, up to now, there is no technique to visualize electrical properties including the electrical anisotropic conductivity, effective extracellular ion-concentration, and electric field using only the tDCS current in-vivo. By measuring the apparent diffusion coefficient (ADC) and the magnetic flux density induced by the tDCS, we propose a method to visualize the electrical properties. We reconstruct the scale parameter, which connects the anisotropic conductivity tensor to the diffusion tensor of water molecules, by introducing a repetitive scheme called the diffusion tensor J-substitution algorithm using the recovered current density and the measured ADCs. We investigate the proposed method to explain why the iterative scheme converges to the internal conductivity. We verified the proposed method with an anesthetized canine brain to visualize electrical properties including the electrical properties by tDCS current.

The electrical conductivity is a passive material property primarily determined by concentrations of charge carriers and their mobility. The macroscopic conductivity of a biological tissue at low frequency may exhibit anisotropy related with its structural directionality. When expressed as a tensor and properly quantified, the conductivity tensor can provide diagnostic information of numerous diseases. Imaging conductivity distributions inside the human body requires probing it by externally injecting conduction currents or inducing eddy currents. At low frequency, the Faraday induction is negligible and it has been necessary in most practical cases to inject currents through surface electrodes. Here we report a novel method to reconstruct conductivity tensor images using an MRI scanner without current injection. This electrodeless method of conductivity tensor imaging (CTI) utilizes B1 mapping to recover a high-frequency isotropic conductivity image which is influenced by contents in both extracellular and intracellular spaces. Multi-b diffusion weighted imaging is then utilized to extract the effects of the extracellular space and incorporate its directional structural property. Implementing the novel CTI method in a clinical MRI scanner, we reconstructed in vivo conductivity tensor images of canine brains. Depending on the details of the implementation, it may produce conductivity contrast images for conductivity weighted imaging (CWI). Clinical applications of CTI and CWI may include imaging of tumor, ischemia, inflammation, cirrhosis, and other diseases. CTI can provide patient-specific models for source imaging, transcranial dc stimulation, deep brain stimulation, and electroporation., Competing Interests: The authors have no conflict of interest to declare.All animal procedures were approved by the institutional animal care and use committee of Kyung Hee University (KHUASP-14-25). All methods were carried out in accordance with the relevant guidelines and regulations.

Techniques for electrical brain stimulation (EBS), in which weak electrical stimulation is applied to the brain, have been extensively studied in various therapeutic brain functional applications. The extracellular fluid in the brain is a complex electrolyte that is composed of different types of ions, such as sodium (Na + ), potassium (K + ), and calcium (Ca + ). Abnormal levels of electrolytes can cause a variety of pathological disorders. In this paper, we present a novel technique to visualize the total electrolyte concentration in the extracellular compartment of biological tissues. The electrical conductivity of biological tissues can be expressed as a product of the concentration and the mobility of the ions. Magnetic resonance electrical impedance tomography (MREIT) investigates the electrical properties in a region of interest (ROI) at low frequencies (below 1 kHz) by injecting currents into the brain region. Combining with diffusion tensor MRI (DT-MRI), we analyze the relation between the concentration of ions and the electrical properties extracted from the magnetic flux density measurements using the MREIT technique. By measuring the magnetic flux density induced by EBS, we propose a fast non-iterative technique to visualize the total extracellular electrolyte concentration (EEC), which is a fundamental component of the conductivity. The proposed technique directly recovers the total EEC distribution associated with the water transport mobility tensor.

Objective: Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI., Methods: To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment., Results: The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox., Conclusion: Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes., Objective: Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI., Methods: To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment., Results: The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox., Conclusion: Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes.

Purpose: To realistically map the electric fields of biological tissues using a diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) method to estimate tissue response during electrical stimulation., Methods: Imaging experiments were performed using chunks of bovine muscle. Two silver wire electrodes were positioned inside the muscle tissue for electrical stimulation. Electric pulses were applied with a 100-V amplitude and 100-μs width using a voltage stimulator. During electrical stimulation, we collected DT-MREIT data from a 3T magnetic resonance imaging scanner. We adopted the projected current density method to calculate the electric field. Based on the relation between the water diffusion tensor and the conductivity tensor, we computed the position-dependent scale factor using the measured magnetic flux density data. Then, a final conductivity tensor map was reconstructed using the multiplication of the water diffusion tensor and the scale factor., Results: The current density images from DT-MREIT data represent the internal current flows that exist not only in the electrodes but also in surrounding regions. The reconstructed electric filed map from our anisotropic conductivity tensor with the projected current density shows coverage that is more than 2 times as wide, and higher signals in both the electrodes and surrounding tissues, than the previous isotropic method owing to the consideration of tissue anisotropy., Conclusions: An electric field map obtained by an anisotropic reconstruction method showed different patterns from the results of the previous isotropic reconstruction method. Since accurate electric field mapping is important to correctly estimate the coverage of the electrical treatment, future studies should include more rigorous validations of the new method through in vivo and in situ experiments.

We present in vivo images of anisotropic electrical conductivity tensor distributions inside canine brains using diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT). The conductivity tensor is represented as a product of an ion mobility tensor and a scale factor of ion concentrations. Incorporating directional mobility information from water diffusion tensors, we developed a stable process to reconstruct anisotropic conductivity tensor images from measured magnetic flux density data using an MRI scanner. Devising a new image reconstruction algorithm, we reconstructed anisotropic conductivity tensor images of two canine brains with a pixel size of 1.25 mm. Though the reconstructed conductivity values matched well in general with those measured by using invasive probing methods, there were some discrepancies as well. The degree of white matter anisotropy was 2 to 4.5, which is smaller than previous findings of 5 to 10. The reconstructed conductivity value of the cerebrospinal fluid was about 1.3 S/m, which is smaller than previous measurements of about 1.8 S/m. Future studies of in vivo imaging experiments with disease models should follow this initial trial to validate clinical significance of DT-MREIT as a new diagnostic imaging modality. Applications in modeling and simulation studies of bioelectromagnetic phenomena including source imaging and electrical stimulation are also promising.

Objective: Anterior cervical discectomy and fusion (ACDF) is a choice of surgical procedure for cervical degenerative diseases associated with radiculopathy or myelopathy. However, the patients undergoing ACDF still have problems. The purpose of the present study is to evaluate the radiologic results of 3 different methods in single-level ACDF., Methods: We conducted a retrospective collection of radiological data from January 2011 to December 2014. A total of 67 patients were included in this study. The patients were divided into 3 groups by operation procedure: using stand-alone cage (group cage, n=20); polyether-ether-ketone (PEEK)-titanium combined anchored cage (group AC, n=21); and anterior cervical cage-plate (group CP, n=26). Global cervical lordosis (C2-C7 Cobb angle), fused segment height, fusion rate, and cervical range of motion (ROM) were measured and analyzed at serial preoperative, postoperative, 6-month, and final 1-year follow-up., Results: Successful bone fusion was achieved in all patients at the final follow-up examination; however, the loss of disc height over 3 mm at the surgical level was observed in 6 patients in group cage. Groups AC and CP yielded significantly better outcomes than group cage in fused segment height and cervical ROM(p=0.01 and p=0.02, respectively). Furthermore, group AC had similar radiologic outcomes to those of group CP., Conclusion: The PEEK-titanium combined anchored cage may be a good alternative procedure in terms of reducing complications induced by plate after ACDF.

Background: Multielectrode arrays (MEAs) have been used to understand electrophysiological network dynamics by recording real-time activity in groups of cells. The extent to which the collection of such data enables hypothesis testing on the level of circuits and networks depends largely on the sophistication of the analyses applied., New Method: We studied the systemic temporal variations of endogenous signaling within an organotypic hippocampal network following theta-burst stimulation (TBS) to the Schaffer collateral-commissural pathways. The recovered current source density (CSD) information from the raw grid of extracellular potentials by using a Gaussian interpolation method increases spatial resolution and avoids border artifacts by numerical differentials., Results: We compared total sink and source currents in DG, CA3, and CA1; calculated accumulated correlation coefficients to compare pre- with post-stimulation CSD dynamics in each region; and reconstructed functional connectivity maps for regional cross-correlations with respect to temporal CSD variations. The functional connectivity maps for potential correlations pre- and post-TBS were compared to investigate the neural network as a whole, revealing differences post-TBS., Comparison With Existing Method(s): Previous MEA work on plasticity in hippocampal evoked potentials has focused on synchronicity across the hippocampus within isolated subregions. Such analyses ignore the complex relationships among diverse components of the hippocampal circuitry, thus failing to capture network-level behaviors integral to understanding hippocampal function., Conclusions: The proposed method of recovering current source density to examine whole-hippocampal function is sensitive to experimental manipulation and is worth further examination in the context of network-level analyses of neural signaling., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Objective: Transcranial direct current stimulation (tDCS) is a neuromodulatory technique for neuropsychiatric diseases and neurological disorders. In the tDCS treatment, dc current is injected into the head through a pair of electrodes attached on the scalp over a target region. A current density imaging method is needed to quantitatively visualize the internal current density distribution during the tDCS treatment., Methods: We developed a novel current density image reconstruction algorithm using 1) a subject specific segmented 3-D head model, 2) diffusion tensor data, and 3) magnetic flux density data induced by the tDCS current. We acquired T1 weighted and diffusion tensor images of the head using the MRI scanner before the treatment. During the treatment, we can measure the induced magnetic flux density data using a magnetic resonance electrical impedance tomography (MREIT) pulse sequence. In this paper, the magnetic flux density data were numerically generated., Results: Numerical simulation results show that the proposed method successfully recovers the current density distribution including the effects of the anisotropic, as well as isotropic conductivity values of different tissues in the head., Conclusion: The proposed current density imaging method using DT-MRI and MREIT can reliably recover cross-sectional images of the current density distribution during the tDCS treatment., Significance: Success of the tDCS treatment depends on a precise determination of the induced current density distribution within different anatomical structures of the brain. Quantitative visualization of the current density distribution in the brain will play an important role in understanding the effects of the electrical stimulation.

A 57-year-old man complained of severe lower back pain and radicular pain in both legs for 1 week after falling from a ladder. Magnetic resonance imaging (MRI) of the spine showed a subdural hematoma (SDH), which was surgically removed. The patient had no back pain or the radicular leg pain at 2 weeks post-surgery. However, he complained of diffuse headaches upon follow-up. Brain computed tomography (CT) and MRI revealed an intracranial SDH, which was immediately removed by surgery. During his 1-year follow-up, he reported that the pain had resolved without recurrence. Simultaneous spinal and intracranial SDH are rare and no standard treatment exists for this condition. This case suggests that it is possible that an intracranial SDH can migrate into the cerebrospinal fluid (CSF) space through an arachnoid tear. CSF circulation allows the intracranial SDH to enter subarachnoid spaces encasing the spinal cord. In order to prevent irreversible damage, surgical intervention should be considered for case of spinal SDH with progressive neurological deficits.

Electrical conductivities of biological tissues show frequency-dependent behaviors, and these values at different frequencies may provide clinically useful diagnostic information. MR-based tissue property mapping techniques such as magnetic resonance electrical impedance tomography (MREIT) and magnetic resonance electrical property tomography (MREPT) are widely used and provide unique conductivity contrast information over different frequency ranges. Recently, a new method for data acquisition and reconstruction for low- and high-frequency conductivity images from a single MR scan was proposed. In this study, we applied this simultaneous dual-frequency range conductivity mapping MR method to evaluate its utility in a designed phantom and two in vivo animal disease models. Magnetic flux density and B(1)(+) phase map for dual-frequency conductivity images were acquired using a modified spin-echo pulse sequence. Low-frequency conductivity was reconstructed from MREIT data by the projected current density method, while high-frequency conductivity was reconstructed from MREPT data by B(1)(+) mapping. Two different conductivity phantoms comprising varying ion concentrations separated by insulating films with or without holes were used to study the contrast mechanism of the frequency-dependent conductivities related to ion concentration and mobility. Canine brain abscess and ischemia were used as in vivo models to evaluate the capability of the proposed method to identify new electrical properties-based contrast at two different frequencies. The simultaneous dual-frequency range conductivity mapping MR method provides unique contrast information related to the concentration and mobility of ions inside tissues. This method has potential to monitor dynamic changes of the state of disease.

Purpose: This study shows the potential of magnetic resonance electrical impedance tomography (MREIT) as a non-invasive RF ablation monitoring technique., Materials and Methods: We prepared bovine muscle tissue with a pair of needle electrodes for RF ablation, a temperature sensor, and two pairs of surface electrodes for conductivity image reconstructions. We used the injected current non-linear encoding with multi-echo gradient recalled echo (ICNE-MGRE) pulse sequence in a series of MREIT scans for conductivity imaging. We acquired magnetic flux density data induced by externally injected currents, while suppressing other phase artefacts. We used an 8-channel RF head coil and 8 echoes to improve the signal-to-noise ratio (SNR) in measured magnetic flux density data. Using the measured data, we reconstructed a time series of 180 conductivity images at every 10.24 s during and after RF ablation., Results: Tissue conductivity values in the lesion increased with temperature during RF ablation. After reaching 60 °C, a steep increase in tissue conductivity values occurred with relatively little temperature increase. After RF ablation, tissue conductivity values in the lesion decreased with temperature, but to values different from those before ablation due to permanent structural changes of tissue by RF ablation., Conclusion: We could monitor temperature and also structural changes in tissue during RF ablation by producing spatio-temporal maps of tissue conductivity values using a fast MREIT conductivity imaging method. We expect that the new monitoring method could be used to estimate lesions during RF ablation and improve the efficacy of the treatment.

Magnetic Resonance Electrical Impedance Tomography (MREIT) is an MRI method that enables mapping of internal conductivity and/or current density via measurements of magnetic flux density signals. The MREIT measures only the z-component of the induced magnetic flux density B = (Bx, By, Bz) by external current injection. The measured noise of Bz complicates recovery of magnetic flux density maps, resulting in lower quality conductivity and current-density maps. We present a new method for more accurate measurement of the spatial gradient of the magnetic flux density gradient (∇ Bz). The method relies on the use of multiple radio-frequency receiver coils and an interleaved multi-echo pulse sequence that acquires multiple sampling points within each repetition time. The noise level of the measured magnetic flux density Bz depends on the decay rate of the signal magnitude, the injection current duration, and the coil sensitivity map. The proposed method uses three key steps. The first step is to determine a representative magnetic flux density gradient from multiple receiver coils by using a weighted combination and by denoising the measured noisy data. The second step is to optimize the magnetic flux density gradient by using multi-echo magnetic flux densities at each pixel in order to reduce the noise level of ∇ Bz and the third step is to remove a random noise component from the recovered ∇ Bz by solving an elliptic partial differential equation in a region of interest. Numerical simulation experiments using a cylindrical phantom model with included regions of low MRI signal to noise ('defects') verified the proposed method. Experimental results using a real phantom experiment, that included three different kinds of anomalies, demonstrated that the proposed method reduced the noise level of the measured magnetic flux density. The quality of the recovered conductivity maps using denoised ∇ Bz data showed that the proposed method reduced the conductivity noise level up to 3-4 times at each anomaly region in comparison to the conventional method.

Magnetic resonance electrical impedance tomography (MREIT) is a promising non-invasive method to visualize a static cross-sectional conductivity and/or current density image by injecting low frequency currents. MREIT measures one component of the magnetic flux density caused by the injected current using a magnetic resonance (MR) scanner. For practical in vivo implementations of MREIT, especially for soft biological tissues where the MR signal rapidly decays, it is crucial to develop a technique for optimizing the magnetic flux density signal by the injected current while maintaining spatial-resolution and contrast. We design an MREIT pulse sequence by applying a spoiled multi-gradient-echo pulse sequence (SPMGE) to the injected current nonlinear encoding (ICNE), which fully injects the current at the end of the read-out gradient. The applied ICNE-SPMGE pulse sequence maximizes the duration of injected current almost up to a repetition time by measuring multiple magnetic flux density data. We analyze the noise level of measured magnetic flux density with respect to the pulse width of injection current and T*(2) relaxation time. In due consideration of the ICNE-SPMGE pulse sequence, using a reference information of T*(2) values in a local region of interest by a short pre-scan data, we predict the noise level of magnetic flux density to be measured for arbitrary repetition time TR. Results from phantom experiment demonstrate that the proposed method can predict the noise level of magnetic flux density for an appropriate TR = 40 ms using a reference scan for TR = 75 ms. The predicted noise level was compared with the noise level of directly measured magnetic flux density data.

Magnetic resonance electrical impedance tomography (MREIT) is an imaging modality capable of visualizing cross-sectional current density and/or conductivity distributions inside an electrically conducting object. It uses an MRI scanner to measure one component of the magnetic flux density induced by an externally injected current through a pair of surface electrodes. For the cases of deep brain stimulation (DBS), electroporation, and radio frequency (RF) ablation, internal electrodes can be used to improve the quality of the MREIT images. In this paper, we propose a new MREIT imaging method using internal electrodes to visualize a current density distribution within a local region around them. To evaluate its performance, we conducted and analyzed a series of numerical simulations and phantom imaging experiments. We compared the reconstructed current density images using the internal electrodes with the obtained using only the external electrodes. We found that the proposed method using the internal electrodes stably determines the current density in the focused region with better accuracy.

Magnetic resonance (MR)-based electrical impedance tomography (MREIT) is a widely used imaging technique that provides high-resolution conductivity images at DC or below the 1 kHz frequency range. Using an MR scanner, this technique injects imaging currents into the human body and measures induced internal magnetic flux density data. By applying the recent progress of MREIT techniques, such as chemical shift artifact correction, multi-echo pulse sequence, and improved reconstruction algorithm, we can successfully reconstruct conductivity images of the human body. Meanwhile, numerous studies reported that the electrical conductivity of human tissues could be inferred from in vitro or ex vivo measurements of different species. However, in vivo tissues may differ from in vitro and/or ex vivo state due to the complicated tissue responses in living organs. In this study, we performed in vivo MREIT imaging of a human lower extremity and compared the resulting conductivity images with ex vivo biological tissue phantom images. The human conductivity images showed unique contrast between two different types of bones, muscles, subcutaneous adipose tissues, and conductive body fluids. Except for muscles and adipose tissues, the human conductivity images showed a similar pattern when compared with phantom results due to the anisotropic characteristic of muscle and the high conductive fluids in the adipose tissue., (© 2014 Wiley Periodicals, Inc.)

Background: In magnetic resonance electrical impedance tomography (MREIT), we reconstruct conductivity images using magnetic flux density data induced by externally injected currents. Since we extract magnetic flux density data from acquired MR phase images, the amount of measurement noise increases in regions of weak MR signals. Especially for local regions of MR signal void, there may occur excessive amounts of noise to deteriorate the quality of reconstructed conductivity images. In this paper, we propose a new conductivity image enhancement method as a postprocessing technique to improve the image quality., Methods: Within a magnetic flux density image, the amount of noise varies depending on the position-dependent MR signal intensity. Using the MR magnitude image which is always available in MREIT, we estimate noise levels of measured magnetic flux density data in local regions. Based on the noise estimates, we adjust the window size and weights of a spatial averaging filter, which is applied to reconstructed conductivity images. Without relying on a partial differential equation, the new method is fast and can be easily implemented., Results: Applying the novel conductivity image enhancement method to experimental data, we could improve the image quality to better distinguish local regions with different conductivity contrasts. From phantom experiments, the estimated conductivity values had 80% less variations inside regions of homogeneous objects. Reconstructed conductivity images from upper and lower abdominal regions of animals showed much less artifacts in local regions of weak MR signals., Conclusion: We developed the fast and simple method to enhance the conductivity image quality by adaptively adjusting the weights and window size of the spatial averaging filter using MR magnitude images. Since the new method is implemented as a postprocessing step, we suggest adopting it without or with other preprocessing methods for application studies where conductivity contrast is of primary concern.

Magnetic resonance electrical impedance tomography (MREIT) is an emerging method to visualize electrical conductivity and/or current density images at low frequencies (below 1 KHz). Injecting currents into an imaging object, one component of the induced magnetic flux density is acquired using an MRI scanner for isotropic conductivity image reconstructions. Diffusion tensor MRI (DT-MRI) measures the intrinsic three-dimensional diffusion property of water molecules within a tissue. It characterizes the anisotropic water transport by the effective diffusion tensor. Combining the DT-MRI and MREIT techniques, we propose a novel direct method for absolute conductivity tensor image reconstructions based on a linear relationship between the water diffusion tensor and the electrical conductivity tensor. We first recover the projected current density, which is the best approximation of the internal current density one can obtain from the measured single component of the induced magnetic flux density. This enables us to estimate a scale factor between the diffusion tensor and the conductivity tensor. Combining these values at all pixels with the acquired diffusion tensor map, we can quantitatively recover the anisotropic conductivity tensor map. From numerical simulations and experimental verifications using a biological tissue phantom, we found that the new method overcomes the limitations of each method and successfully reconstructs both the direction and magnitude of the conductivity tensor for both the anisotropic and isotropic regions.

Background: The spectroscopic conductivity distribution of tissue can help to explain physiological and pathological status. Dual frequency conductivity imaging by combining Magnetic Resonance Electrical Property Tomography (MREPT) and Magnetic Resonance Electrical Impedance Tomography (MREIT) has been recently proposed. MREIT can provide internal conductivity distributions at low frequency (below 1 kHz) induced by an external injecting current. While MREPT can provide conductivity at the Larmor frequency related to the strength of the magnetic field. Despite this potential to describe the membrane properties using spectral information, MREPT and MREIT techniques currently suffer from weak signals and noise amplification as they both reply on differentiation of measured phase data., Methods: We proposed a method to optimize the measured phase signal by finding weighting factors according to the echo signal for MREPT and MREIT using the ICNE (Injected current nonlinear encoding) multi-echo pulse sequence. Our target weights are chosen to minimize the measured noise. The noise standard deviations were precisely analyzed for the optimally weighted magnetic flux density and the phase term of the positive-rotating magnetic field. To enhance the quality of dual-frequency conductivity images, we applied the denoising method based on the reaction-diffusion equation with the estimated noise standard deviations. A real experiment was performed with a hollow cylindrical object made of thin insulating film with holes to control the apparent conductivity using ion mobility and an agarose gel cylinder wrapped in an insulating film without holes to show different spectroscopic conductivities., Results: The ability to image different conductivity characteristics in MREPT and MREIT from a single MR scan was shown by including the two objects with different spectroscopic conductivities. Using the six echo signals, we computed the optimized weighting factors for each echo. The qualities of conductivity images for MREPT and MREIT were improved by optimization of the phase map. The proposed method effectively reduced the random noise artifacts for both MREIT and MREPT., Conclusion: We enhanced the dual conductivity images using the optimally weighted magnetic flux density and the phase term of positive-rotating magnetic field based on the analysis of the noise standard deviations and applying the optimization and denoising methods.

Purpose: To propose a single magnetic resonance scan conductivity imaging technique providing dual-frequency characteristics of tissue conductivity., Methods: Using a modified spin-echo pulse sequence, the magnetic flux density induced by externally injected currents and the B1+ phase map with injected current effects removed were acquired simultaneously. The low-frequency conductivity was reconstructed from the measured magnetic flux density by the projected current density method, while the high-frequency conductivity was reconstructed using the B1+ maps. Three different conductivity phantoms were used to demonstrate low- and high-frequency conductivity characteristics., Results: A conductivity spectrum at two frequencies was successfully acquired with the proposed scheme. Magnetic resonance electrical impedance tomography is advantageous for seeing an anomaly itself wrapped with a thin insulating membrane. In addition, if the membrane is porous, the membrane property can be quantitatively visualized with magnetic resonance electrical impedance tomography. Magnetic resonance electrical properties tomography does not detect such membranes, which enable it to probe things inside an insulating membrane., Conclusion: Considering these pros and cons and also the fact that the conductivity of biological tissue changes with frequency, a dual-frequency conductivity imaging incorporating both magnetic resonance electrical impedance tomography and magnetic resonance electrical properties tomography in future animal and human experiments is suggested., (Copyright © 2013 Wiley Periodicals, Inc.)

Purpose: The aim of this study was to show the potential of magnetic resonance electrical impedance tomography (MREIT) conductivity imaging in terms of its capability to detect ablated lesions and differentiate tissue conditions in liver radiofrequency (RF) ablation., Materials and Methods: RF ablation procedures were performed in bovine livers using a LeVeen RF needle electrode. Ablation lesions were created using a power-controlled mode at 30, 50, and 70 W for 1, 3, and 5 min of exposure time, respectively. After the ablation, the liver was cut into several blocks including the ablated lesion, and positioned inside a phantom filled with agarose gel. Electrodes were attached on the side of the phantom and it was placed inside the MRI bore. For MREIT imaging, multi-spin-echo pulse sequence was used to obtain the magnetic flux density data according to the injection currents., Results: The conductivity of ablation lesions was significantly changed with the increase of exposure time (pKW < 0.01, Kruskal-Wallis test). With RF powers of 30 and 50 W, significant differences between the coagulation necrosis and hyperaemic rim were observed for more than 5 min and 3 min, respectively (pMW < 0.01, Mann-Whitney test). At 70 W, all cases showed significant differences except 3 min (pMW < 0.01). The positive correlation between the exposure time and tissue conductivity was observed in both two ablation areas (pSC < 0.01, Spearman correlation)., Conclusions: This ex vivo feasibility study demonstrates that current MREIT conductivity imaging can detect liver RF ablation lesions without using any contrast media or additional MR scan.

Background: Magnetic resonance electrical impedance tomography (MREIT) has been introduced as a non-invasive method for visualizing the internal conductivity and/or current density of an electrically conductive object by externally injected currents. The injected current through a pair of surface electrodes induces a magnetic flux density distribution inside the imaging object, which results in additional magnetic flux density. To measure the magnetic flux density signal in MREIT, the phase difference approach in an interleaved encoding scheme cancels out the systematic artifacts accumulated in phase signals and also reduces the random noise effect by doubling the measured magnetic flux density signal. For practical applications of in vivo MREIT, it is essential to reduce the scan duration maintaining spatial-resolution and sufficient contrast. In this paper, we optimize the magnetic flux density by using a fast gradient multi-echo MR pulse sequence. To recover the one component of magnetic flux density Bz, we use a coupled partial Fourier acquisitions in the interleaved sense., Methods: To prove the proposed algorithm, we performed numerical simulations using a two-dimensional finite-element model. For a real experiment, we designed a phantom filled with a calibrated saline solution and located a rubber balloon inside the phantom. The rubber balloon was inflated by injecting the same saline solution during the MREIT imaging. We used the multi-echo fast low angle shot (FLASH) MR pulse sequence for MRI scan, which allows the reduction of measuring time without a substantial loss in image quality., Results: Under the assumption of a priori phase artifact map from a reference scan, we rigorously investigated the convergence ratio of the proposed method, which was closely related with the number of measured phase encode set and the frequency range of the background field inhomogeneity. In the phantom experiment with a partial Fourier acquisition, the total scan time was less than 6 seconds to measure the magnetic flux density Bz data with 128×128 spacial matrix size, where it required 10.24 seconds to fill the complete k-space region., Conclusion: Numerical simulation and experimental results demonstrated that the proposed method reduces the scanning time and provides the recovered Bz data comparable to what we obtained by measuring complete k-space data.

Quantitative assessment of the myelin content in white matter (WM) using MRI has become a useful tool for investigating myelin-related diseases, such as multiple sclerosis (MS). Myelin water fraction (MWF) maps can be estimated pixel-by-pixel by a determination of the T₂ or T₂* spectrum from signal decay measurements at each individual image pixel. However, detection of parameters from the measured decay curve, assuming a combination of smooth multi-exponential curves, results in a nonlinear and seriously ill-posed problem. In this paper, we propose a new method to obtain a stable MWF map robust to the presence of noise while sustaining sufficient resolution, which uses weighted combinations of measured decay signals in a spatially independent neighborhood to combine tissues with similar relaxation parameters. To determine optimal weighting factors, we define a spatially independent neighborhood for each pixel and a distance with respect to decay rates that effectively includes pixels with similar decay characteristics, and which therefore have similar relaxation parameters. We recover the MWF values by using optimally weighted decay curves. We use numerical simulations and in vitro and in vivo experimental brain data scanned with a multi-gradient-echo sequence to demonstrate the feasibility of our proposed algorithm and to highlight its advantages compared to the conventional method., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Purpose: To show the feasibility of magnetic resonance electrical impedance tomography (MREIT) conductivity imaging in terms of its capability to provide new contrast information of abscess lesion and characterize time-course variations before and after the induction of brain abscess., Materials and Methods: Brain abscess was induced in healthy beagles by a direct inoculation method using Staphylococcus pseudintermedius. After the induction, four electrodes were attached on the head and the dog was placed inside the magnetic resonance imaging (MRI) bore. Using a current source, we injected a current of amplitude 5 mA and a pulse width of 81 msec. A multi-echo ICNE pulse sequence was used to obtain the magnetic flux density (Bz ) data., Results: The relative conductivity contrast ratios (rCCR, %) of abscess lesion were significantly changed by the postinduction time (P < 0.01). The rCCRs of central abscess lesions were higher than the surrounding area at 6, 12, and 18 hours (P < 0.01). Over 12 hours, the relationship between the induction time and rCCR showed a positive correlation followed by a negative correlation (P < 0.01)., Conclusion: We performed in vivo disease model animal experiments to validate the MREIT technique providing conductivity information of tissues in situ to be utilized in clinical applications., (Copyright © 2012 Wiley Periodicals, Inc.)

MREIT is a new imaging modality that can be used to reconstruct high-resolution conductivity images of the human body. Since conductivity values of cancerous tissues in the breast are significantly higher than those of surrounding normal tissues, breast imaging using MREIT may provide a new noninvasive way of detecting early stage of cancer. In this paper, we present results of experimental and numerical simulation studies of breast MREIT. We built a realistic three-dimensional model of the human breast connected to a simplified model of the chest including the heart and evaluated the ability of MREIT to detect cancerous anomalies in a background material with similar electrical properties to breast tissue. We performed numerical simulations of various scenarios in breast MREIT including assessment of the effects of fat inclusions and effects related to noise levels, such as changing the amplitude of injected currents, effect of added noise and number of averages. Phantom results showed straightforward detection of cancerous anomalies in a background was possible with low currents and few averages. The simulation results showed it should be possible to detect a cancerous anomaly in the breast, while restricting the maximal current density in the heart below published levels for nerve excitation., (Copyright © 2013 Elsevier Inc. All rights reserved.)

In radiofrequency (RF) ablation or hyperthermia, internal temperature measurements and tissue property imaging are important to control their outputs and assess the treatment effect. Recently, magnetic resonance electrical impedance tomography (MREIT), as a non-invasive imaging method of internal conductivity distribution using an MR scanner, has been developed. Its reconstruction algorithm uses measured magnetic flux density induced by injected currents. The MREIT technique has the potential to visualize electrical conductivity of tissue with high spatial resolution and measure relative conductivity variation according to the internal temperature change based on the fact that the electrical conductivity of biological tissues is sensitive to the internal temperature distribution. In this paper, we propose a method to provide a non-invasive alternative to monitor the internal temperature distribution by recovering the electrical conductivity distribution using the MREIT technique. To validate the proposed method, we design a phantom with saline solution and a thin transparency film in a form of a hollow cylinder with holes to create anomalies with different electrical and thermal conductivities controlled by morphological structure. We first prove the temperature maps with respect to spatial and time resolution by solving the thermal conductivity partial differential equation with the real phantom experimental environment. The measured magnetic flux density and the reconstructed conductivity distributions using the phantom experiments were compared to the simulated temperature distribution. The relative temperature variation of two testing objects with respect to the background saline was determined by the relative conductivity contrast ratio (rCCR,%). The relation between the temperature and conductivity measurements using MREIT was approximately linear with better accuracy than 0.22 °C.

Magnetic resonance electrical impedance tomography (MREIT) measures magnetic flux density signals through the use of a magnetic resonance imaging (MRI) in order to visualize the internal conductivity and/or current density. Understanding the reconstruction procedure for the internal current density, we directly measure the second derivative of Bz data from the measured k-space data, from which we can avoid a tedious phase unwrapping to obtain the phase signal of Bz . We determine optimal weighting factors to combine the derivatives of magnetic flux density data, [Symbol: see text](2) Bz , measured using the multi-echo train. The proposed method reconstructs the internal current density using the relationships between the induced internal current and the measured [Symbol: see text](2) Bz data. Results from a phantom experiment demonstrate that the proposed method reduces the scanning time and provides the internal current density, while suppressing the background field inhomogeneity. To implement the real experiment, we use a phantom with a saline solution including a balloon, which excludes other artifacts by any concentration gradient in the phantom.

Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged.

Magnetic resonance electrical impedance tomography (MREIT) is a non-invasive technique for imaging the internal conductivity distribution in tissue within an MRI scanner, utilizing the magnetic flux density, which is introduced when a current is injected into the tissue from external electrodes. This magnetic flux alters the MRI signal, so that appropriate reconstruction can provide a map of the additional z-component of the magnetic field (B(z)) as well as the internal current density distribution that created it. To extract the internal electrical properties of the subject, including the conductivity and/or the current density distribution, MREIT techniques use the relationship between the external injection current and the z-component of the magnetic flux density B = (B(x), B(y), B(z)). The tissue studied typically contains defective regions, regions with a low MRI signal and/or low MRI signal-to-noise-ratio, due to the low density of nuclear magnetic resonance spins, short T(2) or T*(2) relaxation times, as well as regions with very low electrical conductivity, through which very little current traverses. These defective regions provide noisy B(z) data, which can severely degrade the overall reconstructed conductivity distribution. Injecting two independent currents through surface electrodes, this paper proposes a new direct method to reconstruct a regional absolute isotropic conductivity distribution in a region of interest (ROI) while avoiding the defective regions. First, the proposed method reconstructs the contrast of conductivity using the transversal J-substitution algorithm, which blocks the propagation of severe accumulated noise from the defective region to the ROI. Second, the proposed method reconstructs the regional projected current density using the relationships between the internal current density, which stems from a current injection on the surface, and the measured B(z) data. Combining the contrast conductivity distribution in the entire imaging slice and the reconstructed regional projected current density, we propose a direct non-iterative algorithm to reconstruct the absolute conductivity in the ROI. The numerical simulations in the presence of various degrees of noise, as well as a phantom MRI imaging experiment showed that the proposed method reconstructs the regional absolute conductivity in a ROI within a subject including the defective regions. In the simulation experiment, the relative L₂-mode errors of the reconstructed regional and global conductivities were 0.79 and 0.43, respectively, using a noise level of 50 db in the defective region.

Magnetic resonance electrical impedance tomography (MREIT) is a non-invasive method to visualize cross-sectional electrical conductivity and/or current density by measuring a magnetic flux density signal when an electrical current is injected into a subject. In the MREIT system, it is crucial to reduce the scan duration while maintaining spatial resolution and contrast for practical in vivo implementation. The purpose of the study is to optimize the measured magnetic flux density using an interleaved multiple-echo pulse sequence (injected current nonlinear encoding) that acquires each spatial position multiple times, although these pixels vary between echoes in their signal-to-noise ratio due to (a) T*2 decay and (b) the current density passing through the pixel. Using the acquired multiple measured magnetic flux densities, the noise level for the measured magnetic flux density B(z) at each pixel is estimated using the relationship between the intensity of the magnitude and the width of the injected current. We determine an optimal combination of the multiply acquired magnetic flux densities, which optimally reduces the random noise artifacts. We develop a new denoising technique and apply it to a recovered conductivity distribution with a known noise level of the recovered magnetic flux density, which is designed to provide a stable internal conductivity distribution, while sustaining resolution. The proposed method uses three key steps: the first step is optimizing the magnetic flux density by using the multiple-echo magnetic flux densities at each pixel, the second step is estimating the noise level of this optimized magnetic flux density and the third step is applying a denoising technique using the pixel-specific estimated noise level. Numerical simulation experiments using a three-dimensional cylindrical phantom model validated the proposed method. Multiple-echo B(z) data were generated, including in short T*2 or low spin-density regions, as a function of T*2 and the temporal extent of the injected current. In the simulation experiment, comparing between an equally averaged and the optimized B(z) methods, relative L2-mode errors were 0.053 and 0.024, respectively. In an actual imaging experiment of an agarose gel filled with objects of various conductivities and shapes, we acquired six echoes per repetition time. The optimal weighting factors minimized the effects of noise in B(z), and provided reconstructed conductivity maps that suppressed noise artifacts that normally accumulate in the low signal-to-noise-ratio defect regions.