Use of a priori information in estimating tissue resistivities--application to measured data

An extended Kalman filtering approach for the estimation of human head tissue conductivities by using EEG data: a simulation study

In this study, we propose an extended Kalman filter approach for the estimation of the human head tissue conductivities in vivo by using electroencephalogram (EEG) data. Since the relationship between the surface potentials and conductivity distribution is nonlinear, the proposed algorithm first linearizes the system and applies extended Kalman filtering. By using a three-compartment realistic head model obtained from the magnetic resonance images of a real subject, a known dipole assumption and 32 electrode positions, the performance of the proposed method is tested in simulation studies and it is shown that the proposed algorithm estimates the tissue conductivities with less than 1% error in noiseless measurements and less than 5% error when the signal-to-noise ratio is 40 dB or higher. We conclude that the proposed extended Kalman filter approach successfully estimates the tissue conductivities in vivo.

Use of a priori information in estimating tissue resistivities--application to human data in vivo

Accurate resistivity values are necessary to construct reliable numerical models to solve forward/inverse problems in EEG and to localize activity centres in functional brain imaging. These models require accurate geometry and resistivity distribution. The geometry may be extracted from high resolution images. The resistivity distribution may be estimated by using a statistically constrained minimum mean squared error estimator algorithm that has been developed previously by Baysal and Eyüboğlu. In this study, the data are obtained by EEG and MEG sensors during SEF/SEP experiments that involve nine human subjects. The numerical model is realistic, subject-specific and the scalp, the skull and the brain resistivities are estimated. By performing nine different estimations, we found average resistivities of 3.183, 64.559 and 2.833 omega m for scalp, skull and brain, respectively, all under 9% standard deviation. The discrepancies between these results and other works are discussed in detail.

Electrical conductivity imaging by magnetic resonance electrical impedance tomography (MREIT)

Magnetic resonance electrical impedance tomography (MREIT) is a recently developed imaging technique that combines MRI and electrical impedance tomography (EIT). In MREIT, cross-sectional electrical conductivity images are reconstructed from the internal magnetic field density data produced inside an electrically conducting subject when an electrical current is injected into the subject. In this work the results of an electrical conductivity imaging experiment are presented, along with some practical considerations regarding MREIT. The MREIT experiment was performed with a 0.3 Tesla MRI system on a phantom made of two compartments with different electrical conductivities. The current density inside the phantom was measured by the MR current density imaging (MRCDI) technique. The measured current density was then used for conductivity image reconstruction by the J-substitution algorithm. The conductivity phantom images obtained with an injection current of 28mA showed conductivity errors of about 25.5%.