Static conductivity imaging using variational gradient Bz algorithm in magnetic resonance electrical impedance tomography

Chunjae Park, Eun-Jae Park, Eung Je Wool, Ohin Kwon, Jin Keun Seo

Research output: Contribution to journalArticle

60 Citations (Scopus)

Abstract

A new image reconstruction algorithm is proposed to visualize static conductivity images of a subject in magnetic resonance electrical impedance tomography (MREIT). Injecting electrical current into the subject through surface electrodes, we can measure the induced internal magnetic flux density B = (Bx, By, Bz) using an MRI scanner. In this paper, we assume that only the z-component Bz is measurable due to a practical limitation of the measurement technique in MREIT. Under this circumstance, a constructive MREIT imaging technique called the harmonic B z algorithm was recently developed to produce high-resolution conductivity images. The algorithm is based on the relation between ∇ 2Bz and the conductivity requiring the computation of ∇2Bz. Since twice differentiations of noisy B z data tend to amplify the noise, the performance of the harmonic Bz algorithm is deteriorated when the signal-to-noise ratio in measured Bz data is not high enough. Therefore, it is highly desirable to develop a new algorithm reducing the number of differentiations. In this work, we propose the variational gradient Bz algorithm where Bz is differentiated only once. Numerical simulations with added random noise confirmed its ability to reconstruct static conductivity images in MREIT. We also found that it outperforms the harmonic Bz algorithm in terms of noise tolerance. From a careful analysis of the performance of the variational gradient Bz algorithm, we suggest several methods to further improve the image quality including a better choice of basis functions, regularization technique and multilevel approach. The proposed variational framework utilizing only Bz will lead to different versions of improved algorithms.

Original languageEnglish
Pages (from-to)257-269
Number of pages13
JournalPhysiological measurement
Volume25
Issue number1
DOIs
Publication statusPublished - 2004 Feb 1

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Acoustic impedance
Magnetic resonance
Electric Impedance
Tomography
Magnetic Resonance Spectroscopy
Imaging techniques
Noise
Computer-Assisted Image Processing
Signal-To-Noise Ratio
Magnetic flux
Image reconstruction
Magnetic resonance imaging
Image quality
Signal to noise ratio
Electrodes
Computer simulation

All Science Journal Classification (ASJC) codes

  • Biophysics

Cite this

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abstract = "A new image reconstruction algorithm is proposed to visualize static conductivity images of a subject in magnetic resonance electrical impedance tomography (MREIT). Injecting electrical current into the subject through surface electrodes, we can measure the induced internal magnetic flux density B = (Bx, By, Bz) using an MRI scanner. In this paper, we assume that only the z-component Bz is measurable due to a practical limitation of the measurement technique in MREIT. Under this circumstance, a constructive MREIT imaging technique called the harmonic B z algorithm was recently developed to produce high-resolution conductivity images. The algorithm is based on the relation between ∇ 2Bz and the conductivity requiring the computation of ∇2Bz. Since twice differentiations of noisy B z data tend to amplify the noise, the performance of the harmonic Bz algorithm is deteriorated when the signal-to-noise ratio in measured Bz data is not high enough. Therefore, it is highly desirable to develop a new algorithm reducing the number of differentiations. In this work, we propose the variational gradient Bz algorithm where Bz is differentiated only once. Numerical simulations with added random noise confirmed its ability to reconstruct static conductivity images in MREIT. We also found that it outperforms the harmonic Bz algorithm in terms of noise tolerance. From a careful analysis of the performance of the variational gradient Bz algorithm, we suggest several methods to further improve the image quality including a better choice of basis functions, regularization technique and multilevel approach. The proposed variational framework utilizing only Bz will lead to different versions of improved algorithms.",
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Static conductivity imaging using variational gradient Bz algorithm in magnetic resonance electrical impedance tomography. / Park, Chunjae; Park, Eun-Jae; Wool, Eung Je; Kwon, Ohin; Seo, Jin Keun.

In: Physiological measurement, Vol. 25, No. 1, 01.02.2004, p. 257-269.

Research output: Contribution to journalArticle

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