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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136747/ https://www.ncbi.nlm.nih.gov/pubmed/34014939 http://dx.doi.org/10.1371/journal.pone.0251417 |
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author | Lee, Mun Bae Jahng, Geon-Ho Kim, Hyung Joong Kwon, Oh-In |
author_facet | Lee, Mun Bae Jahng, Geon-Ho Kim, Hyung Joong Kwon, Oh-In |
author_sort | Lee, Mun Bae |
collection | PubMed |
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 | Online Article Text |
id | pubmed-8136747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-81367472021-06-02 High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network Lee, Mun Bae Jahng, Geon-Ho Kim, Hyung Joong Kwon, Oh-In PLoS One Research Article 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). Public Library of Science 2021-05-20 /pmc/articles/PMC8136747/ /pubmed/34014939 http://dx.doi.org/10.1371/journal.pone.0251417 Text en © 2021 Lee et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Lee, Mun Bae Jahng, Geon-Ho Kim, Hyung Joong Kwon, Oh-In High-frequency conductivity at Larmor-frequency in human brain using moving local window multilayer perceptron neural network |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8136747/ https://www.ncbi.nlm.nih.gov/pubmed/34014939 http://dx.doi.org/10.1371/journal.pone.0251417 |
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