Gradient-based electrical conductivity imaging using MR phase (original) (raw)
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Electrical conductivity imaging by magnetic resonance electrical impedance tomography (MREIT)
Magnetic Resonance in Medicine, 2003
Magnetic Resonance Electrical Impedance Tomography (MREIT) is a new medical imaging modality providing high resolution static conductivity images based on the current injection MRI technique. MREIT was motivated to deal with the well-known severe ill-posedness of the image reconstruction problem in Electrical Impedance Tomography (EIT). In order to bypass the ill-posed nature in EIT, MREIT takes advantage of an MRI scanner as a tool to capture partial information about magnetic flux density due to internal current density. The conductivity distribution have an effect in change in internal current density pathways that are interrelated with the unknown conductivity distribution. Lately, we have made a significant progress in reconstruction algorithms in MREIT. Experimental MREIT study demonstrates that the reconstructed conductivity image distinguishes different biological tissues in terms of both their shapes and conductivity values. Reconstructed static conductivity images will allow us to obtain internal current density images for any arbitrary injection current and electrode configuration. Very recently, we also initiated a method of reconstructing anisotropic conductivity images.
Magnetic Resonance in Medicine, 2014
Purpose: To develop high-resolution electrical properties tomography (EPT) methods and investigate a gradient-based EPT (gEPT) approach that aims to reconstruct the electrical properties (EP), including conductivity and permittivity, of an imaged sample from experimentally measured B 1 maps with improved boundary reconstruction and robustness against measurement noise. Theory and Methods: Using a multichannel transmit/receive stripline head coil with acquired B 1 maps for each coil element, and by assuming negligible B z component compared to transverse B 1 components, a theory describing the relationship between B 1 field, EP value, and their spatial gradient has been proposed. The final EP images were obtained through spatial integration over the reconstructed EP gradient. Numerical simulation, physical phantom, and in vivo human experiments at 7 T have been conducted to evaluate the performance of the proposed method. Results: Reconstruction results were compared with target EP values in both simulations and phantom experiments. Human experimental results were compared with EP values in literature. Satisfactory agreement was observed with improved boundary reconstruction. Importantly, the proposed gEPT method proved to be more robust against noise when compared to previously described nongradient-based EPT approaches. Conclusion: The proposed gEPT approach holds promises to improve EP mapping quality by recovering the boundary information and enhancing robustness against noise. Magn Reson Med 000:000-000,
Hybrid tomography for conductivity imaging
Inverse Problems, 2012
Hybrid imaging techniques utilize couplings of physical modalities -they are called hybrid, because, typically, the excitation and measurement quantities belong to different modalities. Recently there has been an enormous research interest in this area because these methods promise very high resolution. In this paper we give a review on hybrid tomography methods for electrical conductivity imaging. The reviewed imaging methods utilize couplings between electric, magnetic and ultrasound modalities. By this it is possible to perform high-resolution electrical impedance imaging and to overcome the low-resolution problem of electric impedance tomography.
Multi-echo GRE-based conductivity imaging using Kalman phase estimation method
Magnetic resonance in medicine, 2018
To obtain in vivo electrical conductivity images from multi-echo gradient-echo (mGRE) sequence using a zero-TE phase extrapolation algorithm based on the Kalman method. For estimation of the zero-TE phase from the mGRE data, an iterative algorithm consisting of a combination of the Kalman filter, Kalman smoother, and expectation maximization was implemented and compared with linear extrapolation methods. Simulations were performed for verification, and phantom and in vivo studies were conducted for validation. Compared with the conventional method that linearly extrapolates the zero-TE phase from the mGRE data, the phase estimation of the proposed method was more stable in situations in which nonlinear phase evolution exists. Numerical simulation results showed that the stability is guaranteed under various nonlinearity levels. Phantom study results show that this method provides improved conductivity imaging compared with the conventional methods. In vivo results demonstrate conduc...
Magnetic Resonance in Medicine, 2016
Purpose: To investigate the feasibility of low-frequency conductivity imaging based on measuring the magnetic field due to subject eddy currents induced by switching of MRI zgradients. Methods: We developed a simulation model for calculating subject eddy currents and the magnetic fields they generate (subject eddy fields). The inverse problem of obtaining conductivity distribution from subject eddy fields was formulated as a convection-reaction partial differential equation. For measuring subject eddy fields, a modified spin-echo pulse sequence was used to determine the contribution of subject eddy fields to MR phase images. Results: In the simulations, successful conductivity reconstructions were obtained by solving the derived convectionreaction equation, suggesting that the proposed reconstruction algorithm performs well under ideal conditions. However, the level of the calculated phase due to the subject eddy field in a representative object indicates that this phase is below the noise level and cannot be measured with an uncertainty sufficiently low for accurate conductivity reconstruction. Furthermore, some artifacts other than random noise were observed in the measured phases, which are discussed in relation to the effects of system imperfections during readout. Conclusion: Low-frequency conductivity imaging does not seem feasible using basic pulse sequences such as spin-echo on a clinical MRI scanner.
Conductivity Tensor MR Imaging
The estimation of conductivity distributions in the brain is essential for various biomedical engineering analyses such as obtaining current distributions in electric stimulation and magnetic stimulation, calculating the absorption of electromagnetic waves from mobile phones, and current source estimations in electroencephalography (EEG) and magnetoencephalography (MEG). In addition, tissue characterization is conveyed through conductivity, which depends on cell geometry and intracellular fluid and extracellular fluid compositions. Electrical impedance tomography (EIT), in which surface potentials are measured during applications of currents via surface electrodes, has been applied to obtain conductivity distributions in living bodies (1). However, EIT has relatively low spatial resolution when using a limited number of surface electrodes. Moreover, conductivity distributions of the brain are difficult to obtain because most of the currents do not penetrate the skull due to the low ...
Conductivity Mapping of Biological Tissue Using Diffusion MRI
Annals of The New York Academy of Sciences, 1999
The understanding of electrical injury pathophysiology has benefited greatly from models of electrical current propagation in tissue. 1 However, the accuracy of such models could potentially be improved by incorporating quantitative values for tissue. While it is not possible with the present technology to measure the conductivity of deep tissue noninvasively, it has recently been proposed that the conductivity tensor can be approximated from the diffusion tensor as measured by diffusion MRI. Here, we show how the conductivity tensor can be derived from the water diffusion tensor by a differential effective medium approximation. 2,3 Electrical conductivity maps of human brain white matter are also presented.
Magnetic Resonance-Electrical Properties Tomography by Directly Solving Maxwell’s Curl Equations
Applied Sciences
Magnetic Resonance-Electrical Properties Tomography (MR-EPT) is a method to reconstruct the electrical properties (EPs) of bio-tissues from the measured radiofrequency (RF) field in Magnetic Resonance Imaging (MRI). Current MR-EPT approaches reconstruct the EP profile by solving a second-order partial differential wave equation problem, which is sensitive to noise and can induce large reconstruction artefacts near tissue boundaries and areas with inaccurate field measurements. In this paper, a novel MR-EPT approach is proposed, which is based on a direct solution to Maxwell’s curl equations. The distribution of EPs is calculated by iteratively fitting the RF field calculated by the finite-difference-time-domain (FDTD) technique to the measured values. To solve the time-consuming problem of the iterative fitting, a graphics processing unit (GPU) is used to accelerate the FDTD technique to process the field calculation kernel. The new EPT method was evaluated by investigating a numeri...
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2010
Accurate estimation of the human head conductivity is important for the diagnosis and therapy of brain diseases. Induced Current - Magnetic Resonance Electrical Impedance Tomography (IC-MREIT) is a recently developed non-invasive technique for conductivity estimation. This paper presents a formulation where a low number of material parameters need to be estimated, starting from MR eddy-current field maps. We use a parameterized frequency dependent 4-Cole-Cole material model, an efficient independent impedance method for eddy-current calculations and a priori information through the use of voxel models. The proposed procedure circumvents the ill-posedness of traditional IC-MREIT and computational efficiency is obtained by using an efficient forward eddy-current solver.