A posteriori error estimate and convergence analysis for conductivity image reconstruction in MREIT

Jijun Liu, Jinkeun Seo, Eungje Woo

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14 Citations (Scopus)


Magnetic resonance electrical impedance tomography (MREIT) takes advantage of internal information to solve its nonlinear inverse problem of recovering a conductivity distribution inside an imaging object. When we inject current into the imaging object, there occurs a distribution of internal magnetic flux density B = (Bx, By,Bz). In MREIT we utilize a magnetic resonance imaging scanner with its main magnetic field in the z direction to acquire Bz data. The harmonic Bz algorithm was invented in 2001 to reconstruct cross-sectional conductivity images from Bz data sets subject to multiple injection currents. Utilizing internal Bz data, it overcomes the inherent ill posedness in electrical impedance tomography. We can set up the inverse problem in MREIT as a coefficient identification problem of finding σ appearing in ▽ · (σ▽u) = 0 from acquired data of the z component of ▽ × (σ▽u). The harmonic Bz algorithm has shown an excellent performance in numerical simulations and phantom experiments. Experimental MREIT studies have now reached the stage of in vivo animal and human imaging experiments. However, there is not much work on rigorous mathematical theories of error estimate and convergence analysis yet. The purpose of this paper is to provide a posteriori error estimate in MREIT conductivity image reconstructions. This enables us to evaluate a difference between a reconstructed conductivity image and the unknown true conductivity image. We also describe a convergence analysis of the harmonic Bz algorithm, which improves the previous result of [J. J. Liu, J. K. Seo, M. Sini, and E. J. Woo, SIAM J. Appl. Math., 67 (2007), pp. 1259-1282] in the sense that assumptions on the conductivity are much relaxed.

Original languageEnglish
Pages (from-to)2883-2903
Number of pages21
JournalSIAM Journal on Applied Mathematics
Issue number8
Publication statusPublished - 2010

All Science Journal Classification (ASJC) codes

  • Applied Mathematics


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