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 language | English |
|---|---|
| Pages (from-to) | 257-269 |
| Number of pages | 13 |
| Journal | Physiological measurement |
| Volume | 25 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2004 Feb 1 |
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All Science Journal Classification (ASJC) codes
- Biophysics
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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 journal › Article
TY - JOUR
T1 - Static conductivity imaging using variational gradient Bz algorithm in magnetic resonance electrical impedance tomography
AU - Park, Chunjae
AU - Park, Eun-Jae
AU - Wool, Eung Je
AU - Kwon, Ohin
AU - Seo, Jin Keun
PY - 2004/2/1
Y1 - 2004/2/1
N2 - 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.
AB - 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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U2 - 10.1088/0967-3334/25/1/030
DO - 10.1088/0967-3334/25/1/030
M3 - Article
VL - 25
SP - 257
EP - 269
JO - Physiological Measurement
JF - Physiological Measurement
SN - 0967-3334
IS - 1
ER -