Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.0K
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...
5.0K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

A Phenotype-Embedded Mapper Framework Links Microbiome-Metabolome Interaction Modules to Colorectal Cancer.

Journal of proteome research·2026
Same author

Editorial for "Pre-Radiotherapy Synthetic MRI-Derived Quantitative Heterogeneity and Early Recurrence in Glioblastoma".

Journal of magnetic resonance imaging : JMRI·2026
Same author

YY1/HIF-1α/NDUFA4L2 signaling axis mediates bisphenol A-induced mitochondrial dysfunction and apoptosis in human ovarian granulosa cells.

Reproductive toxicology (Elmsford, N.Y.)·2026
Same author

Age-Specific Contrast Optimization of bSSFP in Fetal Brain.

Magnetic resonance in medicine·2026
Same author

FlexCENT: A frequency-flexible CEST imaging network combining frequency offset encoding and three-dimensional U-Net.

Magnetic resonance letters·2026
Same author

ssNetShift: single-sample metabolic network rewiring reveals hidden prognostic subtypes beyond clinical staging in gastric cancer.

Briefings in bioinformatics·2026

相关实验视频

Updated: Jun 14, 2025

Deep Brain Stimulation with Simultaneous fMRI in Rodents
11:09

Deep Brain Stimulation with Simultaneous fMRI in Rodents

Published on: February 15, 2014

14.0K

通过T2*利用合成数据驱动的深度学习进行映射,提高基于任务的多回声功能磁共振成像的灵敏度.

Yinghe Zhao1, Qinqin Yang1, Shiting Qian1

  • 1Department of Electronic Science, Xiamen University, Xiamen 361005, China.

Brain sciences
|August 29, 2024
PubMed
概括

本研究介绍了多回声fMRI的深度学习方法,以创建更好的T2*地图. 这种方法改善了信号与噪声比和血液氧气水平依赖 (BOLD) 信号变化,以获得更敏感的脑成像.

关键词:
大胆的敏感性.T2* 绘制地图多重回声fMRI的使用合成数据驱动的深度学习.

更多相关视频

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.4K
Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
09:36

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

13.8K

相关实验视频

Last Updated: Jun 14, 2025

Deep Brain Stimulation with Simultaneous fMRI in Rodents
11:09

Deep Brain Stimulation with Simultaneous fMRI in Rodents

Published on: February 15, 2014

14.0K
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases

Published on: July 28, 2013

28.4K
Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation
09:36

Extracting Visual Evoked Potentials from EEG Data Recorded During fMRI-guided Transcranial Magnetic Stimulation

Published on: May 12, 2014

13.8K

科学领域:

  • 神经成像是一种神经成像.
  • 磁共振成像技术 磁共振成像技术

背景情况:

  • 多回声梯度回声平面成像 (ME-GE-EPI) 在功能性MRI (fMRI) 中比单回声 (SE-GE-EPI) 提供更高的灵敏度和稳定性.
  • 直接从ME-fMRI数据中获得的T2*地图提供了准确的动态大脑活动记录,优于回声组合地图.
  • 对于T2*映射的Voxel-wise日志线性拟合 (LLF) 受到图像采集过程中噪声积累的限制.

研究的目的:

  • 引入一种新的合成数据驱动深度学习 (SD-DL) 方法,用于在ME-fMRI中生成T2*地图.
  • 评估SD-DL方法在提高T2*映射精度和灵敏度方面的性能.

主要方法:

  • 开发一种合成数据驱动的深度学习 (SD-DL) 模型,用于T2*地图生成.
  • SD-DL方法应用于多回声 (ME) fMRI数据分析.
  • 将SD-DL衍生T2*图与通过传统的日志线性拟合 (LLF) 获得的图进行比较.

主要成果:

  • SD-DL方法显著提高了ME-fMRI数据的时间信号噪声比 (tSNR).
  • 基于任务的血液氧气水平依赖 (BOLD) 信号变化使用SD-DL方法的T2*地图得到了改进.
  • 多回声独立组件分析 (MEICA) 的性能通过拟议的SD-DL方法得到了提高.

结论:

  • 通过SD-DL方法生成的T2*图表显示,与LLF衍生的图表相比,血液中依赖氧气水平 (BOLD) 的灵敏度更高.
  • SD-DL方法为ME-fMRI中准确而敏感的T2*映射提供了有希望的进步.
  • 这种技术有可能通过ME-fMRI数据改善大脑功能分析.