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

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)01:20

¹³C NMR: Distortionless Enhancement by Polarization Transfer (DEPT)

1.0K
When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
1.0K

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

An 80-channel receive array for 10.5T neuroimaging: Key considerations for SNR optimization.

bioRxiv : the preprint server for biology·2026
Same author

Mesoscale Whole-Brain T<sub>2</sub>*-Weighted and Associated Quantitative MRI in Humans at 10.5 T.

Magnetic resonance in medicine·2026
Same author

Comprehensive 4D Parallel Transmission Spatial-Spectral Pulse Design for Slab-Selective Uniform Water-Selective Excitation: Demonstration in the Human Brain at 7 Tesla.

Magnetic resonance in medicine·2025
Same author

Data Stitching for Dynamic Field Monitoring With NMR Probes.

Magnetic resonance in medicine·2025
Same author

Advancing whole-brain BOLD functional MRI in humans at 10.5 T with motion-robust 3D echo-planar imaging, parallel transmission, and high-density radiofrequency receive coils.

Magnetic resonance in medicine·2025
Same author

Denoising complex-valued diffusion MR images using a two-step, nonlocal principal component analysis approach.

Magnetic resonance in medicine·2025

相关实验视频

Updated: Jun 7, 2025

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

使用两步非局部主要组件分析方法去除复杂值的扩散MR图像.

Xinyu Ye1, Xiaodong Ma2, Ziyi Pan1

  • 1Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China.

bioRxiv : the preprint server for biology
|November 18, 2024
PubMed
概括

这项研究引入了一种新的两步非局部主要组件分析 (PCA) 方法,用于消除扩散张量MRI (DTI) 数据. 这种先进的技术显著提高了图像质量和图谱,即使扩散方向有限.

关键词:
拒绝这种行为,就是拒绝.扩散权重的MRI测试结果低级别的近似计算方法非本地方法 非本地方法

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.4K

相关实验视频

Last Updated: Jun 7, 2025

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
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.6K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.4K

科学领域:

  • 医疗成像医学成像
  • 神经科学是一个神经科学.
  • 生物医学工程 生物医学工程

背景情况:

  • 扩散张力成像 (DTI) 对于神经成像至关重要,但易受噪声的影响.
  • 获取高质量的DTI数据通常需要多个扩散方向,增加扫描时间.
  • 有效的无色化方法对于准确的DTI分析和临床应用至关重要.

研究的目的:

  • 提出和验证一个新的两步非本地主要组件分析 (PCA) 方法,用于DTI报销.
  • 为了证明该方法在改善图像质量的有效性,减少了扩散方向的数量.
  • 增强DTI的实用性,用于需要从有限数据进行参数映射的应用.

主要方法:

  • 实施了两步排噪管道,精确选择高噪声水平的补丁.
  • 集成的g因子规范化和相位稳定,用于可靠的预处理.
  • 使用非本地PCA算法,以最佳收缩为无噪声信号估计.

主要成果:

  • 在模拟和人类数据中大幅提高DTI图像质量.
  • 在降低噪音的同时保持解剖细节方面,超越了现有的基于局部PCA的方法.
  • 与噪音数据相比,实现了对DTI指标和全脑通道图的改进估计.

结论:

  • 拟议的非局部PCA拒绝方法有效地提高了DTI图像质量,使用更少的扩散方向.
  • 这种方法对于优先考虑参数映射且成像量有限的应用程序是有利的.
  • 该方法对推进基于DTI的神经成像研究和诊断具有重大前景.