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.

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.

Regional calibration and electromagnetic conductivity imaging for assessing the dynamics of soil salinity

<p>Electromagnetic conductivity imaging (EMCI) is a state-of-the-art methodology for soil salinity assessment over large areas. It involves the following rationale: (1) use of the electromagnetic induction (EMI) geophysical technique to measure the soil apparent electrical conductivity (EC<sub>a</sub>, mS m<sup>−1</sup>) over an area; (2) inversion of EC<sub>a</sub> to obtain EMCI, which provides the spatial distribution of the soil electrical conductivity (σ, mS m<sup>−1</sup>); (3) calibration process consisting of a regression between σ and the electrical conductivity of the saturated soil paste extract (EC<sub>e</sub>, dS m<sup>−1</sup>), used as a proxy for soil salinity; and (4) conversion of EMCI into salinity maps using the obtained calibration equation.</p><p>In this study, we applied EMCI and a regional calibration in Lezíria Grande de Vila Franca de Xira, located in Portugal. The study area is an important agricultural system where soil faces the risk of salinization due to climate change, as the level and salinity of groundwater are likely to increase as a result of the rise of the sea water level and consequently of the estuary. These changes can also affect the salinity of the irrigation water which is collected upstream of the estuary.</p><p>EMI surveys and soil sampling were carried out between May 2017 and October 2018 at four locations with different salinity levels across the study area. A regional calibration was developed and its ability to predict EC<sub>e</sub> from EMCI was evaluated. The validation analysis showed that EC<sub>e</sub> was predicted with a root mean square error of 3.14 dS m<sup>−1</sup> in a range of 52.35 dS m<sup>−1</sup>, slightly overestimated (−1.23 dS m<sup>−1</sup>), with a strong Lin’s concordance correlation coefficient of 0.94 and high linearity between measured and predicted data (R<sup>2</sup> = 0.88). It was also observed that the prediction ability of the regional calibration is more influenced by spatial variability of data than temporal variability of data.</p><p>Because of the transient nature of data, it was also possible to perform a preliminary qualitative analysis of soil salinity dynamics in the study area, revealing salinity fluctuations related to the input of salts and water either through irrigation, precipitation, or level and salinity of groundwater.</p>

MR-Based Electrical Conductivity Imaging Using Second-Order Total Generalized Variation Regularization

Electrical properties provide essential information for cancer detection and specific absorption rate (SAR) estimation. Magnetic resonance electrical properties tomography (MREPT) is an approach to retrieve the distribution of electrical properties. The conventional method suffers from the locally homogeneous assumption and amplification of noise. In this study, a novel approach was introduced to improve the accuracy and the noise robustness of conductivity imaging. The proposed approach reformulated the central equation of the gradient-based method to avoid the calculation of the Laplacian operator. The equation was regularized using the second-order total generalized variation, which formulates an objective function. The optimization problem was solved by the alternating direction method of multipliers (ADMM) method. The proposed method was validated by the simulation data of the cylindrical phantom and Ella head model, and the performance was compared with existing methods. The results demonstrated that the proposed method reconstructed an accurate conductivity image and alleviated the noise effects. Furthermore, phantom and healthy volunteer experiments were implemented at a 3 Tesla (T) magnetic resonance imaging (MRI) scanner. The results suggested that the developed method can provide solutions for improved conductivity reconstruction and show potential for clinical application.