Non-Invasive in vivo Loss Tangent Imaging: Thermal Sensitivity Estimation at the Larmor Frequency

Visualization of the tissue loss tangent property can provide distinct contrast and offer new information related to tissue electrical properties. A method for non-invasive imaging of the electrical loss tangent of tissue using magnetic resonance imaging (MRI) was demonstrated, and the effect of loss tangent was observed through simulations assuming a hyperthermia procedure. For measurement of tissue loss tangent, radiofrequency field maps (B1+ complex map) were acquired using a double-angle actual flip angle imaging MRI sequence. The conductivity and permittivity were estimated from the complex valued B1+ map using Helmholtz equations. Phantom and ex-vivo experiments were then performed. Electromagnetic simulations of hyperthermia were carried out for observation of temperature elevation with respect to loss tangent. Non-invasive imaging of tissue loss tangent via complex valued B1+ mapping using MRI was successfully conducted. Simulation results indicated that loss tangent is a dominant factor in temperature elevation in the high frequency range during hyperthermia. Knowledge of the tissue loss tangent value can be a useful marker for thermotherapy applications.

Quantitative Conductivity Estimation Error due to Statistical Noise in Complex B1+ Map

PURPOSE: In-vivo conductivity reconstruction using transmit field (B1+) information of MRI was proposed. We assessed the accuracy of conductivity reconstruction in the presence of statistical noise in complex B1 + map and provided a parametric model of the conductivity-to-noise ratio value. MATERIALS AND METHODS: The B1+ distribution was simulated for a cylindrical phantom model. By adding complex Gaussian noise to the simulated B1+ map, quantitative conductivity estimation error was evaluated. The quantitative evaluation process was repeated over several different parameters such as Larmor frequency, object radius and SNR of B1+ map. A parametric model for the conductivity-to-noise ratio was developed according to these various parameters. RESULTS: According to the simulation results, conductivity estimation is more sensitive to statistical noise in B1+ phase than to noise in B1+ magnitude. The conductivity estimate of the object of interest does not depend on the external object surrounding it. The conductivity-to-noise ratio is proportional to the signal-to-noise ratio of the B1+ map, Larmor frequency, the conductivity value itself and the number of averaged pixels. To estimate accurate conductivity value of the targeted tissue, SNR of B1+ map and adequate filtering size have to be taken into account for conductivity reconstruction process. In addition, the simulation result was verified at 3T conventional MRI scanner. CONCLUSION: Through all these relationships, quantitative conductivity estimation error due to statistical noise in B1+ map is modeled. By using this model, further issues regarding filtering and reconstruction algorithms can be investigated for MREPT.