scholarly journals Current Density Imaging Using Directly Measured HarmonicBzData in MREIT

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Chunjae Park ◽  
Oh In Kwon

Magnetic resonance electrical impedance tomography (MREIT) measures magnetic flux density signals through the use of a magnetic resonance imaging (MRI) in order to visualize the internal conductivity and/or current density. Understanding the reconstruction procedure for the internal current density, we directly measure the second derivative ofBzdata from the measuredk-space data, from which we can avoid a tedious phase unwrapping to obtain the phase signal ofBz. We determine optimal weighting factors to combine the derivatives of magnetic flux density data,∇2Bz, measured using the multi-echo train. The proposed method reconstructs the internal current density using the relationships between the induced internal current and the measured∇2Bzdata. Results from a phantom experiment demonstrate that the proposed method reduces the scanning time and provides the internal current density, while suppressing the background field inhomogeneity. To implement the real experiment, we use a phantom with a saline solution including a balloon, which excludes other artifacts by any concentration gradient in the phantom.

2010 ◽  
Vol 55 (11) ◽  
pp. 3177-3199 ◽  
Author(s):  
Yusuf Ziya Ider ◽  
Ozlem Birgul ◽  
Omer Faruk Oran ◽  
Orhan Arikan ◽  
Mark J Hamamura ◽  
...  

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Oh-In Kwon ◽  
Chunjae Park

For an internal conductivity image, magnetic resonance electrical impedance tomography (MREIT) injects an electric current into an object and measures the induced magnetic flux density, which appears in the phase part of the acquired MR image data. To maximize signal intensity, the injected current nonlinear encoding (ICNE) method extends the duration of the current injection until the end of the MR data reading. It disturbs the usual linear encoding of the MRk-space data used in the inverse Fourier transform. In this study, we estimate the magnetic flux density, which is recoverable from nonlinearly encoded MRk-space data by applying a Newton method.


2017 ◽  
Vol 24 (2) ◽  
pp. 422-428 ◽  
Author(s):  
G. Mishra ◽  
Mona Gehlot ◽  
Geetanjali Sharma ◽  
Frederic Trillaud

The magnetic design of a ten-period (each period 14 mm) prototype superconducting undulator is reported using RADIA. The results of modelling the magnetic flux density are presented in an analytical formula. The dependence of the field integrals and phase error on the current density and undulator gap has been calculated, and temperature curves are determined for the models and are compared with earlier reported Moser–Rossmanith fits.


PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254690
Author(s):  
Saurav Z. K. Sajib ◽  
Munish Chauhan ◽  
Oh In Kwon ◽  
Rosalind J. Sadleir

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.


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