J-substitution algorithm in magnetic resonance electrical impedance tomography (MREIT): phantom experiments for static resistivity images

Recently, a new static resistivity image reconstruction algorithm is proposed utilizing internal current density data obtained by magnetic resonance current density imaging technique. This new imaging method is called magnetic resonance electrical impedance tomography (MREIT). The derivation and performance of J-substitution algorithm in MREIT have been reported as a new accurate and high-resolution static impedance imaging technique via computer simulation methods. In this paper, we present experimental procedures, denoising techniques and image reconstructions using a 0.3-tesla (T) experimental MREIT system and saline phantoms. MREIT using J-substitution algorithm effectively utilizes the internal current density information resolving the problem inherent in a conventional EIT, that is, the low sensitivity of boundary measurements to any changes of internal tissue resistivity values. Resistivity images of saline phantoms show an accuracy of 6.8%-47.2% and spatial resolution of 64 /spl times/ 64. Both of them can be significantly improved by using an MRI system with a better signal-to-noise ratio.

[1]  Ozlem Birgul,et al.  A DUAL MODALITY SYSTEM FOR HIGH RESOLUTION-TRUE CONDUCTIVITY IMAGING , 2001 .

[2]  J. Gati,et al.  Imaging of current density and current pathways in rabbit brain during transcranial electrostimulation , 1999, IEEE Transactions on Biomedical Engineering.

[3]  G.J. Saulnier,et al.  An iterative Newton-Raphson method to solve the inverse admittivity problem , 1998, IEEE Transactions on Biomedical Engineering.

[4]  Ohin Kwon,et al.  Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm , 2002, IEEE Transactions on Biomedical Engineering.

[5]  Yves Goussard,et al.  Regularized reconstruction in electrical impedance tomography using a variance uniformization constraint , 1997, IEEE Transactions on Medical Imaging.

[6]  K. Boone,et al.  Imaging with electricity: report of the European Concerted Action on Impedance Tomography. , 1997, Journal of medical engineering & technology.

[7]  Tony F. Chan,et al.  High-Order Total Variation-Based Image Restoration , 2000, SIAM J. Sci. Comput..

[8]  Jari P. Kaipio,et al.  Tikhonov regularization and prior information in electrical impedance tomography , 1998, IEEE Transactions on Medical Imaging.

[9]  E. Woo,et al.  Resistivity image reconstruction using J-substitution algorithm for MREIT , 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[10]  Willis J. Tompkins,et al.  Comparing Reconstruction Algorithms for Electrical Impedance Tomography , 1987, IEEE Transactions on Biomedical Engineering.

[11]  Michael Pidcock,et al.  An image reconstruction algorithm for three-dimensional electrical impedance tomography , 2001, IEEE Transactions on Biomedical Engineering.

[12]  Eung Je Woo,et al.  Impedance tomography using internal current density distribution measured by nuclear magnetic resonance , 1994, Optics & Photonics.

[13]  David Isaacson,et al.  NOSER: An algorithm for solving the inverse conductivity problem , 1990, Int. J. Imaging Syst. Technol..

[14]  John S. Leigh,et al.  Imaging Electrical Current Density Using Nuclear Magnetic Resonance , 1998 .

[15]  R M Henkelman,et al.  Measurement of nonuniform current density by magnetic resonance. , 1991, IEEE transactions on medical imaging.

[16]  M. Joy,et al.  In vivo detection of applied electric currents by magnetic resonance imaging. , 1989, Magnetic resonance imaging.

[17]  R. Henkelman,et al.  Sensitivity of magnetic-resonance current-density imaging , 1992 .

[18]  Eung Je Woo,et al.  A robust image reconstruction algorithm and its parallel implementation in electrical impedance tomography , 1993, IEEE Trans. Medical Imaging.