High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.

Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field associated with the radiofrequency (RF) magnetic fi...

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Autores principales:	Mun Bae Lee, Geon-Ho Jahng, Hyung Joong Kim, Oh-In Kwon
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7a1e30eb865941faa374e644cba0e345
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spelling	oai:doaj.org-article:7a1e30eb865941faa374e644cba0e3452021-11-25T06:23:46ZHigh-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.1932-620310.1371/journal.pone.0251417https://doaj.org/article/7a1e30eb865941faa374e644cba0e3452021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251417https://doaj.org/toc/1932-6203Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field associated with the radiofrequency (RF) magnetic field, the high-frequency conductivity and permittivity distributions inside the human brain have been reconstructed based on the Maxwell's equation. Starting from the Maxwell's equation, the complex permittivity can be described as a second order elliptic partial differential equation. The established reconstruction algorithms have focused on simplifying and/or regularizing the elliptic partial differential equation to reduce the noise artifact. Using the nonlinear relationship between the Maxwell's equation, measured magnetic field, and conductivity distribution, we design a deep learning model to visualize the high-frequency conductivity in the brain, directly derived from measured magnetic flux density. The designed moving local window multi-layer perceptron (MLW-MLP) neural network by sliding local window consisting of neighboring voxels around each voxel predicts the high-frequency conductivity distribution in each local window. The designed MLW-MLP uses a family of multiple groups, consisting of the gradients and Laplacian of measured B1 phase data, as the input layer in a local window. The output layer of MLW-MLP returns the conductivity values in each local window. By taking a non-local mean filtering approach in the local window, we reconstruct a noise suppressed conductivity image while maintaining spatial resolution. To verify the proposed method, we used B1 phase datasets acquired from eight human subjects (five subjects for training procedure and three subjects for predicting the conductivity in the brain).Mun Bae LeeGeon-Ho JahngHyung Joong KimOh-In KwonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251417 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mun Bae Lee Geon-Ho Jahng Hyung Joong Kim Oh-In Kwon High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
description	Magnetic resonance electrical properties tomography (MREPT) aims to visualize the internal high-frequency conductivity distribution at Larmor frequency using the B1 transceive phase data. From the magnetic field perturbation by the electrical field associated with the radiofrequency (RF) magnetic field, the high-frequency conductivity and permittivity distributions inside the human brain have been reconstructed based on the Maxwell's equation. Starting from the Maxwell's equation, the complex permittivity can be described as a second order elliptic partial differential equation. The established reconstruction algorithms have focused on simplifying and/or regularizing the elliptic partial differential equation to reduce the noise artifact. Using the nonlinear relationship between the Maxwell's equation, measured magnetic field, and conductivity distribution, we design a deep learning model to visualize the high-frequency conductivity in the brain, directly derived from measured magnetic flux density. The designed moving local window multi-layer perceptron (MLW-MLP) neural network by sliding local window consisting of neighboring voxels around each voxel predicts the high-frequency conductivity distribution in each local window. The designed MLW-MLP uses a family of multiple groups, consisting of the gradients and Laplacian of measured B1 phase data, as the input layer in a local window. The output layer of MLW-MLP returns the conductivity values in each local window. By taking a non-local mean filtering approach in the local window, we reconstruct a noise suppressed conductivity image while maintaining spatial resolution. To verify the proposed method, we used B1 phase datasets acquired from eight human subjects (five subjects for training procedure and three subjects for predicting the conductivity in the brain).
format	article
author	Mun Bae Lee Geon-Ho Jahng Hyung Joong Kim Oh-In Kwon
author_facet	Mun Bae Lee Geon-Ho Jahng Hyung Joong Kim Oh-In Kwon
author_sort	Mun Bae Lee
title	High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
title_short	High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
title_full	High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
title_fullStr	High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
title_full_unstemmed	High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.
title_sort	high-frequency conductivity at larmor-frequency in human brain using moving local window multilayer perceptron neural network.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7a1e30eb865941faa374e644cba0e345
work_keys_str_mv	AT munbaelee highfrequencyconductivityatlarmorfrequencyinhumanbrainusingmovinglocalwindowmultilayerperceptronneuralnetwork AT geonhojahng highfrequencyconductivityatlarmorfrequencyinhumanbrainusingmovinglocalwindowmultilayerperceptronneuralnetwork AT hyungjoongkim highfrequencyconductivityatlarmorfrequencyinhumanbrainusingmovinglocalwindowmultilayerperceptronneuralnetwork AT ohinkwon highfrequencyconductivityatlarmorfrequencyinhumanbrainusingmovinglocalwindowmultilayerperceptronneuralnetwork
_version_	1718413752497340416

High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network.

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