Magneto-Acousto-Electrical Tomography With Magnetic Induction for Conductivity Reconstruction

Magneto-acousto-electrical tomography (MAET) is an imaging modality proposed to conduct noninvasive electrical conductivity imaging of biological tissue with high spatial resolution. In this study, we present a method of MAET in coil detection mode, which is named as magneto-acousto-electrical tomography with magnetic induction (MAET-MI). Based on the analysis of the mechanism of MAET-MI, we derive a reciprocal theorem and give an integral equation for computing the induced voltage of the coil. The forward problem of MAET-MI can be solved by this integral equation. In the inverse problem of MAET-MI, two steps are taken to reconstruct the conductivity. The first step is to reconstruct the curl of the eddy current density in the reciprocal process by the compression sensing method. And then the conductivity is recovered by the iterative methods such as the Levenberg-Marquardt algorithm. Both the mechanism of MAET-MI and the reconstruction of conductivity are verified by computer simulations. We have also conducted the phantom experiments. The reconstructed images are approximately consistent with the phantom's conductivity. The imaging results prove the ability and the reliability of our proposed methods. It is shown that the relative conductivity distribution can be reconstructed with our proposed reciprocal theorem in MAET-MI modality. Comparing with the traditional MAET, The MAET-MI modality would benefit from the noncontact measurement and be convenient for clinical application.

[1]  Bin He,et al.  A Reconstruction Algorithm of Magnetoacoustic Tomography With Magnetic Induction for an Acoustically Inhomogeneous Tissue , 2014, IEEE Transactions on Biomedical Engineering.

[2]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[3]  Xu Li,et al.  B-Scan Based Acoustic Source Reconstruction for Magnetoacoustic Tomography With Magnetic Induction (MAT-MI) , 2011, IEEE Transactions on Biomedical Engineering.

[4]  Liu Guoqiang,et al.  Conductivity reconstruction algorithms and numerical simulations for magneto—acousto—electrical tomography with piston transducer in scan mode , 2014 .

[5]  Yuanjin Zheng,et al.  Magnetically mediated thermoacoustic imaging toward deeper penetration , 2013 .

[6]  Bin He,et al.  Magnetoacoustic tomography with magnetic induction for imaging electrical impedance of biological tissue , 2006 .

[7]  J. Shah,et al.  Hall effect imaging , 1998, IEEE Transactions on Biomedical Engineering.

[8]  Yuan Xu,et al.  Difference frequency magneto-acousto-electrical tomography (DF-MAET): application of ultrasound-induced radiation force to imaging electrical current density , 2010, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[9]  Bin He,et al.  Magnetoacoustic tomographic imaging of electrical impedance with magnetic induction. , 2007, Applied physics letters.

[10]  S. Mallat,et al.  Adaptive greedy approximations , 1997 .

[11]  D. Isaacson,et al.  A reconstruction algorithm for electrical impedance tomography data collected on rectangular electrode arrays , 1999, IEEE Transactions on Biomedical Engineering.

[12]  H. Griffiths Magnetic induction tomography , 2001 .

[13]  Bin He,et al.  Magnetoacoustic Tomography With Magnetic Induction: Bioimepedance Reconstruction Through Vector Source Imaging , 2013, IEEE Transactions on Medical Imaging.

[14]  Lihong V. Wang,et al.  Universal back-projection algorithm for photoacoustic computed tomography. , 2005 .

[15]  L. Kunyansky A mathematical model and inversion procedure for magneto-acousto-electric tomography , 2012 .

[16]  David Isaacson,et al.  Electrical Impedance Tomography , 1999, SIAM Rev..

[17]  D. Donoho For most large underdetermined systems of equations, the minimal 𝓁1‐norm near‐solution approximates the sparsest near‐solution , 2006 .

[18]  Frédéric Lesage,et al.  The Application of Compressed Sensing for Photo-Acoustic Tomography , 2009, IEEE Transactions on Medical Imaging.

[19]  Bin He,et al.  Magnetoacoustic imaging of human liver tumor with magnetic induction. , 2011, Applied physics letters.

[21]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[22]  Xin Huang,et al.  Magnetoacoustic tomography with current injection , 2013 .

[23]  Byung Il Lee,et al.  Conductivity image reconstruction from defective data in MREIT: numerical Simulation and animal experiment , 2006, IEEE Transactions on Medical Imaging.

[24]  Xu Li,et al.  Multi-excitation Magnetoacoustic Tomography With Magnetic Induction for Bioimpedance Imaging , 2010, IEEE Transactions on Medical Imaging.

[25]  P. Kuchment,et al.  Mathematics of thermoacoustic tomography , 2007, European Journal of Applied Mathematics.

[26]  Y. Xu,et al.  Magneto-acousto-electrical tomography: a potential method for imaging current density and electrical impedance. , 2008, Physiological measurement.

[27]  Y Ziya Ider,et al.  Algebraic reconstruction for 3D magnetic resonance-electrical impedance tomography (MREIT) using one component of magnetic flux density. , 2004, Physiological measurement.

[28]  Bin He,et al.  Magnetoacoustic tomography with magnetic induction (MAT-MI) , 2005, Physics in medicine and biology.