Research On The Detection Method Of Biological Tissue Electrical Impedance Based On Magneto-acoustic-electric Effect

Based on the principle of magneto-acoustic effects in biological tissues,magneto-acoustic tomography with magnetic induction(MAT-MI)and magneto-acoustic-electrical tomography(MAET)both have the capability of differentiating electrical impedance,which integrates the merits of high spatial resolution of ultrasound imaging and high contrast of electrical impedance tomography(EIT).In pervious researches for MAT-MI,biological tissue models are often considered to be conductivity isotropic.However,some biological tissues,such as muscle fibers,myelin sheaths and brain tissues,show significant anisotropies of electrical impedance.As is well known,the distributions of acoustic source strength(ASS)in MAT-MI is affected by the conductivity distribution.Hence,the quantitative analysis of acoustic source strength in tissues with an anisotropic conductivity distribution exhibits great significance in MAT-MI.Meanwhile,the signal-noise ratio(SNR)of the collected MAET signals is always unsatisfactory for the low-level electrical conductivity.Focused ultrasound beams are applied to improve the SNR of signal collection,while the acoustic intensity in the focal zone is still far above the safety limit.Therefore,the SNR improvement with a lower acoustic intensity has significant value for practical application.In this paper,based on the basic principle of MAT-MI and MAET,the analysis of acoustic source strength for MAT-MI in anisotropic tissues and the SNR improvement for MAET with sinusoid-Barker coded excitation are conducted,respectively.(1)The formula of the inducted electrical field and the eddy current is derived for conductivity anisotropic tissues,and theoretical ASS distributions are obtained by combining the mechanism of MAT-MI.And then,numerical simulations are also conducted for the conductivity anisotropic model using the finite element method(FEM).It is proved that,the boundary ASS with respect to the spatial angle shows a periodic vibration,which are composed of an alternating current(AC)fluctuation and a direction current(DC)bias.The amplitude distributions of the AC fluctuation and the DC bias are related to the anisotropy component ratio(ACR)and the conductivity tensor.With the measurements of the ASS around the model,the anisotropic conductivity tensor can be reconstructed by the amplitudes of the AC fluctuation and the DC bias with the conductivity isotropic surrounding medium.(2)Afterwards,based on the ultrasound vibration and propagation theory,the formula of MAET measurement with sinusoid-Barker coded excitation is derived and simplified for a planar piston transduce.With considering the spectral features of the Barker code autocorrelation function,a mismatched filter is also designed and applied for side-lobe suppression.Numerical models for MAET are established with a 13-bit sinusoid-Barker coded excitation,and the MAE signals are calculated for a multi-layered gel phantom.The performances of wave packet recovery with the side-lobe suppression are improved by using the mismatched filter,which reflects the polarity of the conductivity gradient with a higher SNR.In order to verify the feasibility of SNR improvement with the 13-bit sinusoid-Barker coded excitation,an experimental system is built and the signals of three-layer gel models with different conductivities are collected.Comparing with the single-cycle sinusoid excitation,the amplitude of the driving signal can be reduced and the SNR can be enhanced by 10 dB with the 13-bit sinusoid-Barker coded excitation.In this study,the favorable results provide a new method for anisotropic conductivity measurement,and suggest the application potential of MAT-MI in biomedical imaging and nondestructive testing for conductivity anisotropic tissues.The sinusoid-Barker coded modulation method and the mismatched suppression scheme can be applied to MAET measurement to ensure the safety for biological tissues with an improved SNR,and suggest potential applications in biomedical imaging.