Literature DB >> 20400810

Optimization of multiply acquired magnetic flux density B(z) using ICNE-Multiecho train in MREIT.

Hyun Soo Nam1, Oh In Kwon.   

Abstract

The aim of magnetic resonance electrical impedance tomography (MREIT) is to visualize the electrical properties, conductivity or current density of an object by injection of current. Recently, the prolonged data acquisition time when using the injected current nonlinear encoding (ICNE) method has been advantageous for measurement of magnetic flux density data, Bz, for MREIT in the signal-to-noise ratio (SNR). However, the ICNE method results in undesirable side artifacts, such as blurring, chemical shift and phase artifacts, due to the long data acquisition under an inhomogeneous static field. In this paper, we apply the ICNE method to a gradient and spin echo (GRASE) multi-echo train pulse sequence in order to provide the multiple k-space lines during a single RF pulse period. We analyze the SNR of the measured multiple B(z) data using the proposed ICNE-Multiecho MR pulse sequence. By determining a weighting factor for B(z) data in each of the echoes, an optimized inversion formula for the magnetic flux density data is proposed for the ICNE-Multiecho MR sequence. Using the ICNE-Multiecho method, the quality of the measured magnetic flux density is considerably increased by the injection of a long current through the echo train length and by optimization of the voxel-by-voxel noise level of the B(z) value. Agarose-gel phantom experiments have demonstrated fewer artifacts and a better SNR using the ICNE-Multiecho method. Experimenting with the brain of an anesthetized dog, we collected valuable echoes by taking into account the noise level of each of the echoes and determined B(z) data by determining optimized weighting factors for the multiply acquired magnetic flux density data.

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Year:  2010        PMID: 20400810     DOI: 10.1088/0031-9155/55/9/021

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  8 in total

1.  Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

Authors:  Saurav Z K Sajib; Munish Chauhan; Oh In Kwon; Rosalind J Sadleir
Journal:  PLoS One       Date:  2021-07-22       Impact factor: 3.240

2.  Reconstruction of dual-frequency conductivity by optimization of phase map in MREIT and MREPT.

Authors:  Oh In Kwon; Woo Chul Jeong; Saurav Z K Sajib; Hyung Joong Kim; Eung Je Woo; Tong In Oh
Journal:  Biomed Eng Online       Date:  2014-03-08       Impact factor: 2.819

3.  SNR-Enhanced, Rapid Electrical Conductivity Mapping Using Echo-Shifted MRI.

Authors:  Hyunyeol Lee; Jaeseok Park
Journal:  Tomography       Date:  2022-02-05

4.  Current density imaging using directly measured harmonic Bz data in MREIT.

Authors:  Chunjae Park; Oh In Kwon
Journal:  Comput Math Methods Med       Date:  2013-03-20       Impact factor: 2.238

5.  Numerical simulations of MREIT conductivity imaging for brain tumor detection.

Authors:  Zi Jun Meng; Saurav Z K Sajib; Munish Chauhan; Rosalind J Sadleir; Hyung Joong Kim; Oh In Kwon; Eung Je Woo
Journal:  Comput Math Methods Med       Date:  2013-04-29       Impact factor: 2.238

6.  Optimization of magnetic flux density for fast MREIT conductivity imaging using multi-echo interleaved partial fourier acquisitions.

Authors:  Munish Chauhan; Woo Chul Jeong; Hyung Joong Kim; Oh In Kwon; Eung Je Woo
Journal:  Biomed Eng Online       Date:  2013-08-27       Impact factor: 2.819

7.  Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter.

Authors:  Tong In Oh; Hyung Joong Kim; Woo Chul Jeong; Hun Wi; Oh In Kwon; Eung Je Woo
Journal:  Biomed Eng Online       Date:  2014-06-26       Impact factor: 2.819

8.  Anisotropic conductivity tensor imaging for transcranial direct current stimulation (tDCS) using magnetic resonance diffusion tensor imaging (MR-DTI).

Authors:  Mun Bae Lee; Hyung Joong Kim; Eung Je Woo; Oh In Kwon
Journal:  PLoS One       Date:  2018-05-15       Impact factor: 3.240

  8 in total

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