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相关概念视频

Brain Imaging01:14

Brain Imaging

258
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

Magnetic Resonance Imaging

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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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相关实验视频

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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数据驱动的电导率脑成像使用3T核磁共振.

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
概括
此摘要是机器生成的。

人工神经网络 (ANN) 通过使用模拟数据进行更准确的脑电导图来改善磁共振电特性断层扫描 (MR-EPT) 导电性成像. 这种方法对临床应用和疾病检测有前途.

关键词:
导电性大脑成像 导电性大脑成像电气性质断层扫描仪 电气性质断层扫描仪合成MR图像的合成方法非线性导电率估计器非线性导电率估计器基于阶段的EPT重建.

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗成像医学成像
  • 计算电磁学 计算机电磁学

背景情况:

  • 磁共振电特性断层扫描 (MR-EPT) 非侵入性地测量组织电特性 (EPs),如导电性.
  • 组织导电性在临床研究中显示出作为生物标记物的潜力.
  • 传统的MR-EPT导电性重建由于数值假设而面临不准确性.

研究的目的:

  • 开发基于人工神经网络 (ANN) 的非线性导电性估计器,以改善大脑成像.
  • 克服传统基于模型的MR-EPT导电性重建的局限性.
  • 通过模拟,in-silico和in-vivo数据验证ANN方法.

主要方法:

  • 从有限差异时间域 (FDTD) 电磁模拟中对201个合成的T2加权自旋回声 (SE) 数据集进行了ANN训练.
  • 培训数据集包括T2-w SE大小和转接阶段信息.
  • 使用in-silico,志愿者和患者数据对ANN进行了评估,并与传统的基于阶段的EPT方法 (例如S-G Kernel,cr-EPT,Poly-Fit,基于Integral) 相比.

主要成果:

  • 与体实验中的传统方法相比,ANN方法产生了更准确的导电性图,结构保存更好.
  • 基于ANN的重建显示在体内数据 (包括病理) 上的质量,概括性和稳定性得到改善.
  • 该方法在各种信号噪声比 (SNR) 级别和可重复性条件下显示出可靠的性能.

结论:

  • 拟议的基于ANN的MR-EPT导电性估计器显著提高了脑导电性成像的准确性和质量.
  • 该网络从模拟到体外数据 (包括病理) 的概括能力突显了其临床潜力.
  • 这种方法为医疗应用中的定量导电性映射提供了更强大,更准确的替代方案.