Hernandez D, et al. Investig Magn Reson Imaging. 2020 Sep;24(3):95-122. https://doi.org/10.13104/imri.2020.24.3.95

Dielectric relaxation theory describes the complex permittivity of a material in an alternating field; in particular, Debye theory relates the time it takes for an applied field to achieve the maximum polarization and the electrical properties of the material. Although, Debye’s equations were proposed for electrical polarization, in this study, we investigate the correlation between the magnetic longitudinal relaxation time T1 and the complex electrical permittivity of tissue-mimicking phantoms using a 7 T magnetic resonance scanner. We created phantoms that mimicked several human tissues with specific electrical properties. The electrical properties of the phantoms were measured using bench-test equipment. T1 values were acquired from phantoms using MRI. The measured values were fitted with functions based on dielectric estimations, using relaxation times of electrical polarization, and the mixture theory for dielectrics. The results show that, T1 and the real permittivity are correlated; therefore, the correlation can be approximated with a rational function in the case of water-based phantoms. The correlation between index loss and T1 was determined using a fitting function based on the Debye equation and mixture theory equation, in which the fraction of the materials was taken into account. This phantom study and analysis provide an insight into the application relaxation times used for estimating dielectric properties. Currently, the measurement of electrical properties based on dielectric relaxation theory is based on an antenna, sometimes invasive, that irradiates an electric field into a small sample; thus, it is not possible to create a map of electrical properties for a complex structure such as the human body. This study could be further used to compute the electrical properties maps of tissues by scanning images and measuring T1 maps.

The popularity of patch antennas in magnetic resonance imaging (MRI) has reduced because of the large size required for patch antennae to resonate. Since the size of the patch antenna is associated with the wavelength and the wavelengths that are used in MRI are substantially large, large antennas are used. Methods of reducing patch antenna sizes have been proposed; however, these methods reduce the penetration depth and uniformity. In this study, we reduced the area of the patch antenna by 30% by folding the ground and patch planes in a zigzag pattern. The patch antenna produced two main resonant modes. The first mode produced a uniform magnetic field that was used for MRI. The second mode produced a strong and focused electric (|E|)-field, which was used for radiofrequency (RF) heating. Furthermore, we explored the use of a combination of two patch antennas aligned along the z-axis to provide a circular uniform magnetic flux density (|B₁|) field at 300 MHz, which corresponds to the Larmor frequency in the 7T MRI system. In addition, the patch antenna configuration will be used for RF heating hyperthermia operating at 1.06 GHz. The target object was a small rat with insertion of colon cancer. Using the proposed configuration, we achieved |B₁|-field uniformity with a standard deviation of 3% and a temperature increment of 1 °C in the mimic cancer tissue.

Electrical property tomography (EPT) is a noninvasive method that uses magnetic resonance imaging (MRI) to estimate the conductivity and permittivity of tissues, and hence, can be used as a biomarker. One branch of EPT is based on the correlation of water and relaxation time T1 with the conductivity and permittivity of tissues. This correlation was applied to a curve-fitting function to estimate electrical properties, it was found to have a high correlation between permittivity and T1 however the computation of conductivity based on T1 requires to estimate the water content. In this study, we developed multiple phantoms with several ingredients that modify the conductivity and permittivity and explored the use of machine learning algorithms to have a direct estimation of conductivity and permittivity based on MR images and the relaxation time T1. To train the algorithms, each phantom was measured using a dielectric measurement device to acquire the true conductivity and permittivity. MR images were taken for each phantom, and the T1 values were measured. Then, the acquired data were tested using curve fitting, regression learning, and neural fit models to estimate the conductivity and permittivity values based on the T1 values. In particular, the regression learning algorithm based on Gaussian process regression showed high accuracy with a coefficient of determination R² of 0.96 and 0.99 for permittivity and conductivity, respectively. The estimation of permittivity using regression learning demonstrated a lower mean error of 0.66% compared to the curve fitting method, which resulted in a mean error of 3.6%. The estimation of conductivity also showed that the regression learning approach had a lower mean error of 0.49%, whereas the curve fitting method resulted in a mean error of 6%. The findings suggest that utilizing regression learning models, specifically Gaussian process regression, can result in more accurate estimations for both permittivity and conductivity compared to other methods.

Radio wave propagation of three types of birdcage transmit/receive radio‐frequency (RF) coils (lowpass filter, highpass filter, and bandpass filter configurations) was analysed for mouse body magnetic resonance imaging at main magnetic fields of 1.5, 3.0, 4.7, 7.0, 9.4, and 11.7 T (Larmor frequencies of 63.87, 127.74, 200, 300, 400, and 500 MHz) in terms of magnetic (| B ₁ |) field sensitivity and homogeneity. The observed radio wave propagation in the central axial | B ₁ | field, as influenced by the biological subject, was calculated numerically in the finite‐difference time‐domain method and compared with assessment criteria using the mean value and standard deviation. The results for different Larmor frequencies RF shielding cage configurations, and biological subject loading versus unloading were compared and are discussed in detail.