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Data‐driven electrical conductivity brain imaging using 3 T MRI
Magnetic resonance electrical properties tomography (MR‐EPT) is a non‐invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley & Sons, Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502651/ https://www.ncbi.nlm.nih.gov/pubmed/37466309 http://dx.doi.org/10.1002/hbm.26421 |
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author | Jung, Kyu‐Jin Mandija, Stefano Cui, Chuanjiang Kim, Jun‐Hyeong Al‐masni, Mohammed A. Meerbothe, Thierry G. Park, Mina van den Berg, Cornelis A. T. Kim, Dong‐Hyun |
author_facet | Jung, Kyu‐Jin Mandija, Stefano Cui, Chuanjiang Kim, Jun‐Hyeong Al‐masni, Mohammed A. Meerbothe, Thierry G. Park, Mina van den Berg, Cornelis A. T. Kim, Dong‐Hyun |
author_sort | Jung, Kyu‐Jin |
collection | PubMed |
description | Magnetic resonance electrical properties tomography (MR‐EPT) is a non‐invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model‐based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)‐based non‐linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2‐weighted spin‐echo (SE) data obtained from the finite‐difference time‐domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2‐w SE magnitude and transceive phase information. The proposed method was tested three in‐silico and in‐vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase‐based EPT reconstruction methods were used that ignore [Formula: see text] magnitude information, such as Savitzky–Golay kernel combined with Gaussian filter (S‐G Kernel), phase‐based convection‐reaction EPT (cr‐EPT), magnitude‐weighted polynomial‐fitting phase‐based EPT (Poly‐Fit), and integral‐based phase‐based EPT (Integral‐based). From the in‐silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in‐silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground‐truth conductivity (2.00, 0.30, 0.50 S/m) than the integral‐based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In‐vivo ANN‐based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in‐vivo data and pathologies. The reported in‐vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in‐vivo pathologies, thus demonstrating its potential for clinical applications. |
format | Online Article Text |
id | pubmed-10502651 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | John Wiley & Sons, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-105026512023-09-16 Data‐driven electrical conductivity brain imaging using 3 T MRI Jung, Kyu‐Jin Mandija, Stefano Cui, Chuanjiang Kim, Jun‐Hyeong Al‐masni, Mohammed A. Meerbothe, Thierry G. Park, Mina van den Berg, Cornelis A. T. Kim, Dong‐Hyun Hum Brain Mapp Research Articles Magnetic resonance electrical properties tomography (MR‐EPT) is a non‐invasive measurement technique that derives the electrical properties (EPs, e.g., conductivity or permittivity) of tissues in the radiofrequency range (64 MHz for 1.5 T and 128 MHz for 3 T MR systems). Clinical studies have shown the potential of tissue conductivity as a biomarker. To date, model‐based conductivity reconstructions rely on numerical assumptions and approximations, leading to inaccuracies in the reconstructed maps. To address such limitations, we propose an artificial neural network (ANN)‐based non‐linear conductivity estimator trained on simulated data for conductivity brain imaging. Network training was performed on 201 synthesized T2‐weighted spin‐echo (SE) data obtained from the finite‐difference time‐domain (FDTD) electromagnetic (EM) simulation. The dataset was composed of an approximated T2‐w SE magnitude and transceive phase information. The proposed method was tested three in‐silico and in‐vivo on two volunteers and three patients' data. For comparison purposes, various conventional phase‐based EPT reconstruction methods were used that ignore [Formula: see text] magnitude information, such as Savitzky–Golay kernel combined with Gaussian filter (S‐G Kernel), phase‐based convection‐reaction EPT (cr‐EPT), magnitude‐weighted polynomial‐fitting phase‐based EPT (Poly‐Fit), and integral‐based phase‐based EPT (Integral‐based). From the in‐silico experiments, quantitative analysis showed that the proposed method provides more accurate and improved quality (e.g., high structural preservation) conductivity maps compared to conventional reconstruction methods. Representatively, in the healthy brain in‐silico phantom experiment, the proposed method yielded mean conductivity values of 1.97 ± 0.20 S/m for CSF, 0.33 ± 0.04 S/m for WM, and 0.52 ± 0.08 S/m for GM, which were closer to the ground‐truth conductivity (2.00, 0.30, 0.50 S/m) than the integral‐based method (2.56 ± 2.31, 0.39 ± 0.12, 0.68 ± 0.33 S/m). In‐vivo ANN‐based conductivity reconstructions were also of improved quality compared to conventional reconstructions and demonstrated network generalizability and robustness to in‐vivo data and pathologies. The reported in‐vivo brain conductivity values were in agreement with literatures. In addition, the proposed method was observed for various SNR levels (SNR levels = 10, 20, 40, and 58) and repeatability conditions (the eight acquisitions with the number of signal averages = 1). The preliminary investigations on brain tumor patient datasets suggest that the network trained on simulated dataset can generalize to unforeseen in‐vivo pathologies, thus demonstrating its potential for clinical applications. John Wiley & Sons, Inc. 2023-07-19 /pmc/articles/PMC10502651/ /pubmed/37466309 http://dx.doi.org/10.1002/hbm.26421 Text en © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Research Articles Jung, Kyu‐Jin Mandija, Stefano Cui, Chuanjiang Kim, Jun‐Hyeong Al‐masni, Mohammed A. Meerbothe, Thierry G. Park, Mina van den Berg, Cornelis A. T. Kim, Dong‐Hyun Data‐driven electrical conductivity brain imaging using 3 T MRI |
title | Data‐driven electrical conductivity brain imaging using 3 T MRI
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title_full | Data‐driven electrical conductivity brain imaging using 3 T MRI
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title_fullStr | Data‐driven electrical conductivity brain imaging using 3 T MRI
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title_full_unstemmed | Data‐driven electrical conductivity brain imaging using 3 T MRI
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title_short | Data‐driven electrical conductivity brain imaging using 3 T MRI
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title_sort | data‐driven electrical conductivity brain imaging using 3 t mri |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10502651/ https://www.ncbi.nlm.nih.gov/pubmed/37466309 http://dx.doi.org/10.1002/hbm.26421 |
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