Feasibility of Magnetic Resonance-Based Conductivity Imaging as a Tool to Estimate the Severity of Hypoxic-Ischemic Brain Injury in the First Hours After Cardiac Arrest.

BACKGROUND: Early identification of the severity of hypoxic-ischemic brain injury (HIBI) after cardiac arrest can be used to help plan appropriate subsequent therapy. We evaluated whether conductivity of cerebral tissue measured using magnetic resonance-based conductivity imaging (MRCI), which provides contrast derived from the concentration and mobility of ions within the imaged tissue, can reflect the severity of HIBI in the early hours after cardiac arrest. METHODS: Fourteen minipigs were resuscitated after 5 min or 12 min of untreated cardiac arrest. MRCI was performed at baseline and at 1 h and 3.5 h after return of spontaneous circulation (ROSC). RESULTS: In both groups, the conductivity of cerebral tissue significantly increased at 1 h after ROSC compared with that at baseline (P = 0.031 and 0.016 in the 5-min and 12-min groups, respectively). The increase was greater in the 12-min group, resulting in significantly higher conductivity values in the 12-min group (P = 0.030). At 3.5 h after ROSC, the conductivity of cerebral tissue in the 12-min group remained increased (P = 0.022), whereas that in the 5-min group returned to its baseline level. CONCLUSIONS: The conductivity of cerebral tissue was increased in the first hours after ROSC, and the increase was more prominent and lasted longer in the 12-min group than in the 5-min group. Our findings suggest the promising potential of MRCI as a tool to estimate the severity of HIBI in the early hours after cardiac arrest.

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

BACKGROUND: Liver 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. PURPOSE: To 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 TYPE: Prospective phantom and animal study. ANIMAL MODEL: Fibrosis was induced by weekly intraperitoneal injection of dimethylnitrosamine (DMN) in 45 male Sprague-Dawley rats. FIELD STRENGTH/SEQUENCE: 3T MRI with a multispin-echo pulse sequence. ASSESSMENT: The 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 TESTS: One-way analysis of variance (ANOVA) and a two-sample t-test were performed. RESULTS: Liver 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 CONCLUSION: The 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 2 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:554-563.

Comparison of Five Conductivity Tensor Models and Image Reconstruction Methods Using MRI.

Imaging of the electrical conductivity distribution inside the human body has been investigated for numerous clinical applications. The conductivity tensors of biological tissue have been obtained from water diffusion tensors by applying several models, which may not cover the entire phenomenon. Recently, a new conductivity tensor imaging (CTI) method was developed through a combination of B1 mapping, and multi-b diffusion weighted imaging. In this study, we compared the most recent CTI method with the four existing models of conductivity tensors reconstruction. Two conductivity phantoms were designed to evaluate the accuracy of the models. Applied to five human brains, the conductivity tensors using the four existing models and CTI were imaged and compared with the values from the literature. The conductivity image of the phantoms by the CTI method showed relative errors between 1.10% and 5.26%. The images by the four models using DTI could not measure the effects of different ion concentrations subsequently due to prior information of the mean conductivity values. The conductivity tensor images obtained from five human brains through the CTI method were comparable to previously reported literature values. The images by the four methods using DTI were highly correlated with the diffusion tensor images, showing a coefficient of determination (R2) value of 0.65 to 1.00. However, the images by the CTI method were less correlated with the diffusion tensor images and exhibited an averaged R2 value of 0.51. The CTI method could handle the effects of different ion concentrations as well as mobilities and extracellular volume fractions by collecting and processing additional B1 map data. It is necessary to select an application-specific model taking into account the pros and cons of each model. Future studies are essential to confirm the usefulness of these conductivity tensor imaging methods in clinical applications, such as tumor characterization, EEG source imaging, and treatment planning for electrical stimulation.

Validation of conductivity tensor imaging using giant vesicle suspensions with different ion mobilities.

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.

Evaluation of Hepatoprotective Effect of Curcumin on Liver Cirrhosis Using a Combination of Biochemical Analysis and Magnetic Resonance-Based Electrical Conductivity Imaging.

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.

Measurement of extracellular volume fraction using magnetic resonance-based conductivity tensor imaging.

Conductivity tensor imaging (CTI) using MRI is an advanced method that can non-invasively measure the electrical properties of living tissues. The contrast of CTI is based on underlying hypothesis about the proportionality between the mobility and diffusivity of ions and water molecules inside tissues. The experimental validation of CTI in both in vitro and in vivo settings is required as a reliable tool to assess tissue conditions. The changes in extracellular space can be indicators for disease progression, such as fibrosis, edema, and cell swelling. In this study, we conducted a phantom imaging experiment to test the feasibility of CTI for measuring the extracellular volume fraction in biological tissue. To mimic tissue conditions with different extracellular volume fractions, four chambers of giant vesicle suspension (GVS) with different vesicle densities were included in the phantom. The reconstructed CTI images of the phantom were compared with the separately-measured conductivity spectra of the four chambers using an impedance analyzer. Moreover, the values of the estimated extracellular volume fraction in each chamber were compared with those measured by a spectrophotometer. As the vesicle density increased, we found that the extracellular volume fraction, extracellular diffusion coefficient, and low-frequency conductivity decreased, while the intracellular diffusion coefficient slightly increased. On the other hand, the high-frequency conductivity could not clearly distinguish the four chambers. The extracellular volume fraction measured by the spectrophotometer and CTI method in each chamber were quite comparable, i.e., (1.00, 0.98 ± 0.01), (0.59, 0.63 ± 0.02), (0.40, 0.40 ± 0.05), and (0.16, 0.18 ± 0.02). The prominent factor influencing the low-frequency conductivity at different GVS densities was the extracellular volume fraction. Further studies are needed to validate the CTI method as a tool to measure the extracellular volume fractions in living tissues with different intracellular and extracellular compartments.

Estimation of brain tissue response by electrical stimulation in a subject-specific model implemented by conductivity tensor imaging.

Electrical stimulation such as transcranial direct current stimulation (tDCS) is widely used to treat neuropsychiatric diseases and neurological disorders. Computational modeling is an important approach to understand the mechanisms underlying tDCS and optimize treatment planning. When applying computational modeling to treatment planning, uncertainties exist due to insufficient conductivity information inside the brain. In this feasibility study, we performed in vivo MR-based conductivity tensor imaging (CTI) experiments on the entire brain to precisely estimate the tissue response to the electrical stimulation. A recent CTI method was applied to obtain low-frequency conductivity tensor images. Subject-specific three-dimensional finite element models (FEMs) of the head were implemented by segmenting anatomical MR images and integrating a conductivity tensor distribution. The electric field and current density of brain tissues following electrical stimulation were calculated using a conductivity tensor-based model and compared to results using an isotropic conductivity model from literature values. The current density by the conductivity tensor was different from the isotropic conductivity model, with an average relative difference |rD| of 52 to 73%, respectively, across two normal volunteers. When applied to two tDCS electrode montages of C3-FP2 and F4-F3, the current density showed a focused distribution with high signal intensity which is consistent with the current flowing from the anode to the cathode electrodes through the white matter. The gray matter tended to carry larger amounts of current densities regardless of directional information. We suggest this CTI-based subject-specific model can provide detailed information on tissue responses for personalized tDCS treatment planning.

Effects of Irradiation on Brain Tumors Using MR-Based Electrical Conductivity Imaging.

Ionizing radiation delivers sufficient energy inside the human body to create ions, which kills cancerous tissues either by damaging the DNA directly or by creating charged particles that can damage the DNA. Recent magnetic resonance (MR)-based conductivity imaging shows higher sensitivity than other MR techniques for evaluating the responses of normal tissues immediately after irradiation. However, it is still necessary to verify the responses of cancer tissues to irradiation by conductivity imaging for it to become a reliable tool in evaluating therapeutic effects in clinical practice. In this study, we applied MR-based conductivity imaging to mouse brain tumors to evaluate the responses in irradiated and non-irradiated tissues during the peri-irradiation period. Absolute conductivities of brain tissues were measured to quantify the irradiation effects, and the percentage changes were determined to estimate the degree of response. The conductivity of brain tissues with irradiation was higher than that without irradiation for all tissue types. The percentage changes of tumor tissues with irradiation were clearly different than those without irradiation. The measured conductivity and percentage changes between tumor rims and cores to irradiation were clearly distinguished. The contrast of the conductivity images following irradiation may reflect the response to the changes in cellularity and the amounts of electrolytes in tumor tissues.

Identification of Brain Damage after Seizures Using an MR-Based Electrical Conductivity Imaging Method.

Previous imaging studies have shown the morphological malformation and the alterations of ionic mobility, water contents, electrical properties, or metabolites in seizure brains. Magnetic resonance electrical properties tomography (MREPT) is a recently developed technique for the measurement of electrical tissue properties with a high frequency that provides cellular information regardless of the cell membrane. In this study, we examined the possibility of MREPT as an applicable technique to detect seizure-induced functional changes in the brain of rats. Ultra-high field (9.4 T) magnetic resonance imaging (MRI) was performed, 2 h, 2 days, and 1 week after the injection of N-methyl-D-aspartate (NMDA; 75 mg/kg). The conductivity images were reconstructed from B1 phase images using a magnetic resonance conductivity imaging (MRCI) toolbox. The high-frequency conductivity was significantly decreased in the hippocampus among various brain regions of NMDA-treated rats. Nissl staining showed shrunken cell bodies and condensed cytoplasm potently at 2 h after NMDA treatment, and neuronal cell loss at all time points in the hippocampus. These results suggest that the reduced electrical conductivity may be associated with seizure-induced neuronal loss in the hippocampus. Magnetic resonance (MR)-based electrical conductivity imaging may be an applicable technique to non-invasively identify brain damage after a seizure.

Image-Based Evaluation of Irradiation Effects in Brain Tissues by Measuring Absolute Electrical Conductivity Using MRI.

Radiation-induced injury is damage to normal tissues caused by unintentional exposure to ionizing radiation. Image-based evaluation of tissue damage by irradiation has an advantage for the early assessment of therapeutic effects by providing sensitive information on minute tissue responses in situ. Recent magnetic resonance (MR)-based electrical conductivity imaging has shown potential as an effective early imaging biomarker for treatment response and radiation-induced injury. However, to be a tool for evaluating therapeutic effects, validation of its reliability and sensitivity according to various irradiation conditions is required. We performed MR-based electrical conductivity imaging on designed phantoms to confirm the effect of ionizing radiation at different doses and on in vivo mouse brains to distinguish tissue response depending on different doses and the elapsed time after irradiation. To quantify the irradiation effects, we measured the absolute conductivity of brain tissues and calculated relative conductivity changes based on the value of pre-irradiation. The conductivity of the phantoms with the distilled water and saline solution increased linearly with the irradiation doses. The conductivity of in vivo mouse brains showed different time-course variations and residual contrast depending on the irradiation doses. Future studies will focus on validation at long-term time points, including early and late delayed response and evaluation of irradiation effects in various tissue types.

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

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

In Vivo Measurement of Brain Tissue Response After Irradiation: Comparison of T2 Relaxation, Apparent Diffusion Coefficient, and Electrical Conductivity.

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.

Realistic Electric Field Mapping of Anisotropic Muscle During Electrical Stimulation Using a Combination of Water Diffusion Tensor and Electrical Conductivity.

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.