In magnetic resonance imaging-based electrical properties tomography (MREPT), tissue electrical properties (EPs) are derived from the spatial variation of the transmit RF field (B1+). Here we derive theoretically the relationship between the signal-to-noise ratio (SNR) of the electrical properties obtained by MREPT and the SNR of the input B1+ data, under the assumption that that latter is much greater than unity, and the noise in B1+ at different voxels is statistically independent. It is shown that for a given B1+ data, the SNR of both electrical conductivity and relative permittivity is proportional to the square of the linear dimension of the region of interest (ROI) over which the EPs are determined, and to the square root of the number of voxels in the ROI. The relationship also shows how the SNR varies with the main magnetic field (B0) strength. The predicted SNR is verified through numerical simulations on a cylindrical phantom with an analytically calculated B1+ map, and is found to provide explanation of certain aspects of previous experimental results in literature. Our SNR formula can be used to estimate minimum input data SNR and ROI size required to obtain tissue EP maps of desired quality.

The capability of magnetic resonance imaging (MRI) to produce spatially resolved estimation of tissue electrical properties (EPs) in vivo has been a subject of much recent interest. In this work we introduce a method to map tissue EPs from low-flip-angle, zero-echo-time (ZTE) imaging. It is based on a new theoretical formalism that allows calculation of EPs from the product of transmit and receive radio-frequency (RF) field maps. Compared to conventional methods requiring separation of the transmit RF field (B1+) from acquired MR images, the proposed method has such advantages as: (i) reduced theoretical error, (ii) higher acquisition speed, and (iii) flexibility in choice of different transmit and receive RF coils. The method is demonstrated in electrical conductivity and relative permittivity mapping in a salt water phantom, as well as in-vivo measurement of brain conductivity in healthy volunteers. The phantom results show the validity and scan-time efficiency of the proposed method applied to a piece-wise homogeneous object. Quality of in-vivo EP results was limited by reconstruction errors near tissue boundaries, which highlights need for image segmentation in EP mapping in a heterogeneous medium. Our results show the feasibility of rapid EP mapping from MRI without B1+ mapping.

Tissue conductivity and permittivity are critical to understanding local radio frequency (RF) power deposition during magnetic resonance imaging (MRI). These electrical properties are also important in treatment planning of RF thermotherapy methods (e.g. RF hyperthermia). The electrical properties may also have diagnostic value as malignant tissues have been reported to have higher conductivity and higher relative permittivity than surrounding healthy tissue. In this study, we consider imaging conductivity and permittivity using MRI transmit field maps (B1+ maps) at 3.0 Tesla. We formulate efficient methods to calculate conductivity and relative permittivity from 2-dimensional B1+ data and validate the methods with simulated B1+ maps, generated at 128 MHz. Next we use the recently introduced Bloch-Siegert shift B1+ mapping method to acquire B1+ maps at 3.0 Tesla and demonstrate conductivity and relative permittivity images that successfully identify contrast in electrical properties.

Purpose To investigate permittivity and conductivity of cancerous and normal tissues, their correlation to the apparent diffusion coefficient (ADC), and the specificity that they could add to cancer detection. Methods Breast adenocarcinomas and prostate carcinomas were induced in rats. Conductivity and permittivity measurements were performed in tumors and muscle tissue in the anesthetized animals, using a dielectric probe and an impedance analyzer, between 50 and 270MHz. The correlations between ADC’s (measured at 3T) and probe-measured conductivity values were investigated. Frequency dependent discriminant functions were computed, to assess the value that each of the three parameters adds to cancer detection. Results 27%/12%/7% permittivity contrast was observed between tumors and normal tissue at 64/128/270 MHz, respectively. Relatively frequency independent, 15–20% conductivity contrast was noted between cancerous and normal tissue. Strong negative correlation was observed between tissue conductivity and ADC. While permittivity had the strongest discriminatory power at 1.5T, it became comparable to ADC at 3T, and less important than ADC at 270 MHz. Conclusion Conductivity measurements offered limited advantages in separating cancer from normal tissue beyond what ADC already provided; conversely, permittivity added separation power when added to the discriminant function. The moderately high cancerous tissue permittivity and conductivity impose strong constraints on the capability of MRI-based tissue electrical property measurements.

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