Selaka Bandara Bulumulla

On conductivity, permittivity, apparent diffusion coefficient, and their usefulness as cancer markers at MRI frequencies.

To investigate the 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.

Tissue Electrical Property Mapping From Zero Echo-Time Magnetic Resonance Imaging

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: 1) reduced theoretical error, 2) higher acquisition speed, and 3) 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.

Theoretical Investigation of Random Noise-Limited Signal-to-Noise Ratio in MR-Based Electrical Properties Tomography

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 the 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 the 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.