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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.
Afiliação
  • Jung KJ; Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
  • Mandija S; Computational Imaging Group for MR Therapy and Diagnostics, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Cui C; Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Kim JH; Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
  • Al-Masni MA; Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.
  • Meerbothe TG; Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul, Republic of Korea.
  • Park M; Computational Imaging Group for MR Therapy and Diagnostics, University Medical Center Utrecht, Utrecht, The Netherlands.
  • van den Berg CAT; Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
  • Kim DH; Department of Radiology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea.
Hum Brain Mapp ; 44(15): 4986-5001, 2023 10 15.
Article em En | MEDLINE | ID: mdl-37466309
ABSTRACT
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 B 1 + 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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Imageamento por Ressonância Magnética Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Processamento de Imagem Assistida por Computador / Imageamento por Ressonância Magnética Idioma: En Ano de publicação: 2023 Tipo de documento: Article