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Related Concept Videos

Brain Imaging01:14

Brain Imaging

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Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...
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Magnetic Resonance Imaging01:24

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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Related Experiment Video

Updated: Jul 23, 2025

A Multimodal Imaging- and Stimulation-based Method of Evaluating Connectivity-related Brain Excitability in Patients with Epilepsy
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Data-driven electrical conductivity brain imaging using 3 T MRI.

Kyu-Jin Jung1, Stefano Mandija2,3, Chuanjiang Cui1

  • 1Department of Electrical and Electronic Engineering, Yonsei University, Seoul, Republic of Korea.

Human Brain Mapping
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

An artificial neural network (ANN) improves magnetic resonance electrical properties tomography (MR-EPT) conductivity imaging by using simulated data for more accurate brain conductivity maps. This method shows promise for clinical applications and disease detection.

Keywords:
Conductivity brain imagingElectrical properties tomographyMR image synthetizationNon-linear conductivity estimatorPhase-based EPT reconstruction

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Area of Science:

  • Biomedical Engineering
  • Medical Imaging
  • Computational Electromagnetics

Background:

  • Magnetic resonance electrical properties tomography (MR-EPT) non-invasively measures tissue electrical properties (EPs) like conductivity.
  • Tissue conductivity shows potential as a biomarker in clinical studies.
  • Conventional MR-EPT conductivity reconstructions face inaccuracies due to numerical assumptions.

Purpose of the Study:

  • To develop an artificial neural network (ANN)-based non-linear conductivity estimator for improved brain imaging.
  • To overcome limitations of traditional model-based MR-EPT conductivity reconstructions.
  • To validate the ANN method using simulated, in-silico, and in-vivo data.

Main Methods:

  • Trained an ANN on 201 synthesized T2-weighted spin-echo (SE) datasets from finite-difference time-domain (FDTD) electromagnetic simulations.
  • The training dataset included T2-w SE magnitude and transceive phase information.
  • Evaluated the ANN against conventional phase-based EPT methods (e.g., S-G Kernel, cr-EPT, Poly-Fit, Integral-based) using in-silico, volunteer, and patient data.

Main Results:

  • The ANN method produced more accurate conductivity maps with better structural preservation compared to conventional methods in in-silico experiments.
  • ANN-based reconstructions showed improved quality, generalizability, and robustness on in-vivo data, including pathologies.
  • The method demonstrated reliable performance across various signal-to-noise ratio (SNR) levels and repeatability conditions.

Conclusions:

  • The proposed ANN-based MR-EPT conductivity estimator significantly enhances the accuracy and quality of brain conductivity imaging.
  • The network's ability to generalize from simulated to in-vivo data, including pathologies, highlights its clinical potential.
  • This approach offers a more robust and accurate alternative for quantitative conductivity mapping in medical applications.