Experimental implementation of a new method of imaging anisotropic electric conductivities

Ma, Weijing; DeMonte, Tim P.; Nachman, Adrian; Elsaid, Nahla M. H.; Joy, Michael

doi:10.1109/embc.2013.6611028

Cited by 9 publications

(6 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike the method by Ma et al (2013) where all the three components of B = (B x , B y , B z ) are used, we are restricted to the B z data in this paper to avoid rotating the imaging object inside the MRI scanner. Instead of using J = 1 μ 0 ∇ × B, therefore, we computed the projected current density J P from the measured B z data subject to two injection currents.…”

Section: Discussionmentioning

confidence: 99%

“…The term D −1 J i can be defined as the pseudo-current as suggested by Ma et al (2013). With a current density in place of D −1 J i , the relation ( 24) is also the basis to recover the isotropic conductivity from two internal current densities in CDII (Hasanov et al 2008).…”

Section: Reconstruction Of Conductivity Tensormentioning

confidence: 99%

“…In this paper, we adopt the linear relation between the conductivity tensor C and the water diffusion tensor D (Tuch et al 2001) and propose a novel algorithm to reconstruct the anisotropic conductivity tensor by combining the DT-MRI and MREIT techniques without any referred extra-cellular conductivity and diffusivity information. This idea was first suggested by Ma et al (2013) as diffusion tensor current density impedance imaging (DT-CD-II), where DT-MRI is combined with current density impedance imaging (CDII) for anisotropic conductivity tensor imaging. Since they use CDII, measurements of the vector quantity B are required.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anisotropic conductivity tensor imaging in MREIT using directional diffusion rate of water molecules

et al. 2014

View full text Add to dashboard Cite

Magnetic resonance electrical impedance tomography (MREIT) is an emerging method to visualize electrical conductivity and/or current density images at low frequencies (below 1 KHz). Injecting currents into an imaging object, one component of the induced magnetic flux density is acquired using an MRI scanner for isotropic conductivity image reconstructions. Diffusion tensor MRI (DT-MRI) measures the intrinsic three-dimensional diffusion property of water molecules within a tissue. It characterizes the anisotropic water transport by the effective diffusion tensor. Combining the DT-MRI and MREIT techniques, we propose a novel direct method for absolute conductivity tensor image reconstructions based on a linear relationship between the water diffusion tensor and the electrical conductivity tensor. We first recover the projected current density, which is the best approximation of the internal current density one can obtain from the measured single component of the induced magnetic flux density. This enables us to estimate a scale factor between the diffusion tensor and the conductivity tensor. Combining these values at all pixels with the acquired diffusion tensor map, we can quantitatively recover the anisotropic conductivity tensor map. From numerical simulations and experimental verifications using a biological tissue phantom, we found that the new method overcomes the limitations of each method and successfully reconstructs both the direction and magnitude of the conductivity tensor for both the anisotropic and isotropic regions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Reconstruction Of Conductivity Tensormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anisotropic conductivity tensor imaging in MREIT using directional diffusion rate of water molecules

et al. 2014

View full text Add to dashboard Cite

show abstract

“…A new method, namely diffusion tensor-MREIT (DT-MREIT), has been proposed to reconstruct the anisotropic conductivity and current density distribution of biological tissues (Ma et al 2013, Kwon et al 2014. DT-MREIT is a combination of diffusion tensor imaging (DTI) and MREIT and is based on the linear relationship between the electrical conductivity (C ) and water self-diffusion (D ) tensors in a porous medium (Tuch et al 2001) as:…”

Section: Introductionmentioning

confidence: 99%

Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT

et al. 2021

View full text Add to dashboard Cite

Diffusion tensor-magnetic resonance electrical impedance tomography (DT-MREIT) is an imaging modality to obtain low-frequency anisotropic conductivity distribution employing diffusion tensor imaging and MREIT techniques. DT-MREIT is based on the linear relationship between the conductivity and water self-diffusion tensors in a porous medium, like the brain white matter. Several DT-MREIT studies in the literature provide cross-sectional anisotropic conductivity images of tissue phantoms, canine brain, and the human brain. In these studies, the conductivity tensor images are reconstructed using the diffusion tensor and current density data acquired by injecting two linearly independent current patterns. In this study, a novel reconstruction algorithm is devised for DT-MREIT to reconstruct the conductivity tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The proposed method is evaluated utilizing simulated measurements and physical experiments. The results obtained show the successful reconstruction of the anisotropic conductivity distribution using the proposed single current DT-MREIT.

show abstract

“…Kwon et al [19] proposed a J-substitution algorithm, which is an iterative algorithm. Assuming knowledge of the magnitude of only one current density |H| = |γ ∇u|, the problem was studied in [26][27][28] (see the latter reference for a review) in the isotropic case and more recently in [10,21] in the anisotropic case under knowledge of the anisotropic part (the reconstructed conformal factor is hence also scalar). In [14,20], Nachman et al and Lee independently found a explicit reconstruction formula for visualizing log γ at each point in a domain.…”

Section: Introductionmentioning

confidence: 99%

Inverse anisotropic conductivity from internal current densities

2014

View full text Add to dashboard Cite

This paper concerns the reconstruction of an anisotropic conductivity tensor γ from internal current densities of the form J = γ∇u, where u solves a second-order elliptic equation ∇ · (γ∇u) = 0 on a bounded domain X with prescribed boundary conditions. A minimum number of such functionals equal to n + 2, where n is the spatial dimension, is sufficient to guarantee a local reconstruction. We show that γ can be uniquely reconstructed with a loss of one derivative compared to errors in the measurement of J. In the special case where γ is scalar, it can be reconstructed with no loss of derivatives. We provide a precise statement of what components may be reconstructed with a loss of zero or one derivatives.

show abstract

Experimental implementation of a new method of imaging anisotropic electric conductivities

Cited by 9 publications

References 19 publications

Anisotropic conductivity tensor imaging in MREIT using directional diffusion rate of water molecules

Anisotropic conductivity tensor imaging in MREIT using directional diffusion rate of water molecules

Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT

Inverse anisotropic conductivity from internal current densities

Contact Info

Product

Resources

About