Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm

Kwon, Ohin; Woo, Eung Je; Yoon, Jeong-Rock; Seo, Jin Keun

doi:10.1109/10.979355

Cited by 254 publications

(125 citation statements)

References 15 publications

(18 reference statements)

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“…It has been verified that, in order to achieve the unique solution for the MREIT inverse problem, at least two currents need to be injected into the subject (Kwon et al 2002, Ïder et al 2003). Let J 0 and V 0 represent the measured current density and voltage difference between the two current injection electrodes induced by the injected current I 0 .…”

Section: Methodsmentioning

confidence: 99%

“…J 1 and V 1 are the corresponding measurements induced by another injected current I 1 . The J-substitution algorithm (Kwon et al 2002, Liu et al 2009) and harmonic B z algorithm (Oh et al 2003), two widely used algorithms for solving the MREIT inverse problem based on current density and magnetic flux density measurements respectively, may then be applied. The steps below were adopted in the J-substitution algorithm to reconstruct the conductivity distribution inside the subject iteratively:

Step 1.

…”

Section: Methodsmentioning

confidence: 99%

“…For more details regarding these two reconstruction algorithms, please refer to Kwon et al (2002) and Oh et al (2003).…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A feasibility study of magnetic resonance electrical impedance tomography for prostate cancer detection

Liu

Zhang

2014

Physiol. Meas.

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Magnetic resonance electrical impedance tomography (MREIT) is an imaging technique that reconstructs the conductivity distribution inside the subject using magnetic flux density or current density measurements acquired by a magnetic resonance imaging (MRI) system. Since the primary prostate cancer diagnostic method, prostate biopsy, has limited accuracy in cancer diagnosis and malignant tissues have shown significantly different electrical properties from normal or benign tissues, MREIT has potential application in prostate cancer detection. The feasibility of utilizing MREIT in detecting prostate cancer was evaluated via a series of well-designed computer simulations in the present study. MREIT techniques with three different electrode configurations (external, trans-rectal, and trans-urethral electrode arrays) and two different reconstruction algorithms (J-substitution algorithm and harmonic Bz algorithm) were successfully developed. The performance of different MREIT techniques were evaluated and compared based on the imaging accuracy of the reconstructed conductivity distribution in the prostate. Without the presence of noise, the external MREIT achieves a better imaging accuracy than the two endo-MREIT (trans-rectal and trans-urethral) techniques, while the trans-urethral MREIT achieves the best imaging accuracy in noisy environments. We also found that the J-substitution reconstruction algorithm consistently offered better imaging accuracy than the harmonic Bz algorithm. When Gaussian distributed random noise with a standard deviation of 0.25 nT was added, the relative errors (RE) between the reconstructed and target conductivity distributions inside the prostate were observed to be 14.18% and 17.35% by the trans-urethral MREIT with the J-substitution and harmonic Bz algorithms respectively. The lower REs of 9.64% and 11.17% were achieved respectively when the standard deviation of noise was reduced to 0.05 nT. The simulation results demonstrate the feasibility of applying MREIT for prostate cancer detection.

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Section: Methodsmentioning

confidence: 99%

Step 1.

…”

Section: Methodsmentioning

confidence: 99%

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A feasibility study of magnetic resonance electrical impedance tomography for prostate cancer detection

Liu

Zhang

2014

Physiol. Meas.

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“…[7][8][9] To avoid rotating the imaging object inside a MRI scanner, the latest image reconstruction algorithms in MREIT have focused on producing the isotropic or equivalent isotropic conductivity image using only B z data. 4,[10][11][12][13][14][15][16][17][18][19][20] For the isotropic conductivity reconstruction, we inject at least two currents in order to generate internal current flows with different pathways. Since there may exist infinitely many isotropic conductivity distributions which generate a same internal current density, at least two independent injection currents are definitely beneficial to determine the internal conductivity.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of apparent orthotropic conductivity tensor image using magnetic resonance electrical impedance tomography

Sajib

Kim

Jeong

et al. 2015

Journal of Applied Physics

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Magnetic resonance electrical impedance tomography visualizes current density and/or conductivity distributions inside an electrically conductive object. Injecting currents into the imaging object along at least two different directions, induced magnetic flux density data can be measured using a magnetic resonance imaging scanner. Without rotating the object inside the scanner, we can measure only one component of the magnetic flux density denoted as Bz. Since the biological tissues such as skeletal muscle and brain white matter show strong anisotropic properties, the reconstruction of anisotropic conductivity tensor is indispensable for the accurate observations in the biological systems. In this paper, we propose a direct method to reconstruct an axial apparent orthotropic conductivity tensor by using multiple Bz data subject to multiple injection currents. To investigate the anisotropic conductivity properties, we first recover the internal current density from the measured Bz data. From the recovered internal current density and the curl-free condition of the electric field, we derive an over-determined matrix system for determining the internal absolute orthotropic conductivity tensor. The over-determined matrix system is designed to use a combination of two loops around each pixel. Numerical simulations and phantom experimental results demonstrate that the proposed algorithm stably determines the orthotropic conductivity tensor.

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“…These include electrical impedance tomography (EIT) [1]–[4], magnetic induction tomography (MIT) [5] and magnetic resonance electrical impedance tomography (MREIT) [6]–[10]. Among these approaches, EIT needs to inject current into the tissue with multiple surface electrodes.…”

Section: Introductionmentioning

confidence: 99%

A Reconstruction Algorithm of Magnetoacoustic Tomography With Magnetic Induction for an Acoustically Inhomogeneous Tissue

Zhou¹,

Zhu²,

He³

2014

IEEE Trans. Biomed. Eng.

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Magnetoacoustic tomography with Magnetic Induction (MAT-MI) is a noninvasive electrical conductivity imaging approach that measures ultrasound wave induced by magnetic stimulation, for reconstructing the distribution of electrical impedance in biological tissue. Existing reconstruction algorithms for MAT-MI are based on the assumption that the acoustic properties in the tissue are homogeneous. However, the tissue in most parts of human body, has heterogeneous acoustic properties, which leads to potential distortion and blurring of small buried objects in the impedance images. In the present study, we proposed a new algorithm for MAT-MI to image the impedance distribution in tissues with inhomogeneous acoustic speed distributions. With a computer head model constructed from MR images of a human subject, a series of numerical simulation experiments were conducted. The present results indicate that the inhomogeneous acoustic properties of tissues in terms of speed variation can be incorporated in MAT-MI imaging.

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Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm

Cited by 254 publications

References 15 publications

A feasibility study of magnetic resonance electrical impedance tomography for prostate cancer detection

A feasibility study of magnetic resonance electrical impedance tomography for prostate cancer detection

Reconstruction of apparent orthotropic conductivity tensor image using magnetic resonance electrical impedance tomography

A Reconstruction Algorithm of Magnetoacoustic Tomography With Magnetic Induction for an Acoustically Inhomogeneous Tissue

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