Realistic Electric Field Mapping of Anisotropic Muscle During Electrical Stimulation Using a Combination of Water Diffusion Tensor and Electrical Conductivity / 대한배뇨장애요실금학회지

PURPOSE: To realistically map the electric fields of biological tissues using a diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) method to estimate tissue response during electrical stimulation. METHODS: Imaging experiments were performed using chunks of bovine muscle. Two silver wire electrodes were positioned inside the muscle tissue for electrical stimulation. Electric pulses were applied with a 100-V amplitude and 100-μs width using a voltage stimulator. During electrical stimulation, we collected DT-MREIT data from a 3T magnetic resonance imaging scanner. We adopted the projected current density method to calculate the electric field. Based on the relation between the water diffusion tensor and the conductivity tensor, we computed the position-dependent scale factor using the measured magnetic flux density data. Then, a final conductivity tensor map was reconstructed using the multiplication of the water diffusion tensor and the scale factor. RESULTS: The current density images from DT-MREIT data represent the internal current flows that exist not only in the electrodes but also in surrounding regions. The reconstructed electric filed map from our anisotropic conductivity tensor with the projected current density shows coverage that is more than 2 times as wide, and higher signals in both the electrodes and surrounding tissues, than the previous isotropic method owing to the consideration of tissue anisotropy. CONCLUSIONS: An electric field map obtained by an anisotropic reconstruction method showed different patterns from the results of the previous isotropic reconstruction method. Since accurate electric field mapping is important to correctly estimate the coverage of the electrical treatment, future studies should include more rigorous validations of the new method through in vivo and in situ experiments.

A Two-step MREIT Algorithm for Head Tissues Based on Radial Basic Function Neural Network / 航天医学与医学工程

Objective To develop a new Two-step magnetic resonance electrical impedance tomography(MREIT)algorithm based on radial basic function(RBF)neural network for imaging electrical impedance distribution of a head.Methods Firstly,the magnetic resonance imaging(MRI)system with high resolution was used to set up 3D model of the object and to identify the boundaries of different tissues.Then RBF MREIT algorithm was applied to estimate piece-wise homogeneous impedance values of those tissues,respectively.Furthermore,the impedance of each element within each region of the FEM model was estimated according to the RBF genetic algorithm method based on the piece-wise constant impedance.Results Computer simulations were conducted in a three-sphere head model(scalp-skull-brain,SSB)and the simulation results showed the applicability and feasibility of the present Two-step MREIT algorithm in imaging continuous electrical impedance distribution within the head.Conclusion The present Two-step MREIT algorithm is an effective method for imaging the continuous electrical impedance distribution within the human head.