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.

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 to 3.25 with mean value of 2.43, whereas that of the gray matter was in the range of 1.12 and 1.19 with 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.

Latest experimental results in magnetic resonance electrical impedance tomography (MREIT) demonstrated high-resolution in vivo conductivity imaging of animal and human subjects using imaging currents of 5 to 9 mA. Externally injected imaging currents induce magnetic flux density distributions, which are affected by a conductivity distribution. Since we extract the induced magnetic flux density images from MR phase images, it is essential to reduce noise in the phase images. In vivo human and disease model animal experiments require reduction of imaging current amplitudes and scan times. In this article, we investigate a multi-echo based MREIT pulse sequence where we utilize a remaining time after the first echo within one TR to obtain more echo signals. It also allows us to prolong the total current injection time. From phantom and animal imaging experiments, we found that this method significantly reduces the noise level in measured magnetic flux density images. We describe experimental validation of the multi-echo sequence by comparing its performance with a single-echo method using 3 mA imaging currents. The proposed method will be advantageous for an imaging region with long T2 values such as the brain and knee. Depending on T2 values, we suggest using two or three echoes in future experimental studies. Magn Reson Med, 2011.

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 (B_z) 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. J. Magn. Reson. Imaging 2013;38:189–197.

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.

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. 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. 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. 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.