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

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 combination of ketones and NAD<sup>+</sup> precursor preserves white matter integrity in mild cognitive impairment.

Alzheimer's & dementia (New York, N. Y.)·2026
Same author

Spinal cord imaging for multiple sclerosis: Advances, priorities, and opportunities.

Multiple sclerosis (Houndmills, Basingstoke, England)·2026
Same author

A comparative study of deep learning for cortical lesion MRI segmentation with explainability analysis in multiple sclerosis.

NeuroImage. Clinical·2026
Same author

Automatic multiple sclerosis lesion segmentation in the spinal cord using 3 T and 7 T MP2RAGE images.

Multiple sclerosis and related disorders·2026
Same author

Serum Glial Fibrillary Acidic Protein and Retinal Neuronal Loss as Additive Prognostic Markers of Disability in Multiple Sclerosis.

Neurology(R) neuroimmunology & neuroinflammation·2026
Same author

Structure-function multilayer network integration and cognition in multiple sclerosis.

Network neuroscience (Cambridge, Mass.)·2026
Same journal

Intraoperative contrast-enhanced ultrasound features of progressive multifocal leukoencephalopathy: a case report.

Frontiers in neuroimaging·2026
Same journal

SliceMap: a binary classification-driven 2D pipeline for detecting discriminative candidate regions in brain MRI.

Frontiers in neuroimaging·2026
Same journal

Pulvinar pathways as skip connections in deep neural networks for vision.

Frontiers in neuroimaging·2026
Same journal

Evaluating the methodological quality of coordinate-based meta-analyses: the qual-CBMA checklist.

Frontiers in neuroimaging·2026
Same journal

Imaging research, diagnosis, and treatment advances of post-stroke cognitive impairment.

Frontiers in neuroimaging·2026
Same journal

Anatomically constrained volumetric smoothing enhances fMRI reliability while avoiding smoothing artifacts.

Frontiers in neuroimaging·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K

在扩散MRI下方采样:一个捆绑特定的DTI和NODDI研究.

Federico Spagnolo1, Susanna Gobbi1, Enikő Zsoldos1

  • 1Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland.

Frontiers in neuroimaging
|April 12, 2024
PubMed
概括
此摘要是机器生成的。

将扩散MRI (dMRI) 量减少高达30%,保持了从扩散张力成像 (DTI) 和神经元定向分散和密度成像 (NODDI) 的可比的白质微结构指标. 这种优化大大缩短了神经退行性疾病研究的获取时间.

关键词:
在 DTI 中,DTI 是指DTI.这就是为什么MRI是MRI.诺迪尔是什么意思收购时间 收购时间神经退行性疾病的神经退行性疾病

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
Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

11.7K

相关实验视频

Last Updated: Jun 28, 2025

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
17:06

Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging

Published on: November 8, 2012

26.2K
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
Diffusion Imaging in the Rat Cervical Spinal Cord
10:46

Diffusion Imaging in the Rat Cervical Spinal Cord

Published on: April 7, 2015

11.7K

科学领域:

  • 神经成像是一种神经成像.
  • 扩散磁共振成像 (dMRI) 是一种磁共振成像.
  • 白物质微结构分析 白物质微结构分析

背景情况:

  • 多外dMRI对于表征神经退行性疾病中的白质至关重要.
  • 非标准化的协议导致冗余测量和延长扫描时间.
  • 优化dMRI获取对于大型临床研究至关重要.

研究的目的:

  • 研究降低梯度方向对扩散张力成像 (DTI) 和神经元定向分散和密度成像 (NODDI) 度量的影响.
  • 为了确定更短的dMRI协议的可行性,而不会损害数据完整性.

主要方法:

  • 利用了三项纵向研究中124名健康对照者的dMRI数据.
  • 开发了一种内部算法,以减少每个shell的梯度方向的数量.
  • 估计DTI和NODDI对六个临床相关的白质捆绑的措施.

主要成果:

  • 在30%的采样中,分数异位变异性 (FA) 和平均扩散性 (MD) 显示了最小的L1中位距离 (分别高达3.92%和4.31%).
  • 在50%的抽样时,FA和MD的L1距离中位数分别为3.90%和5.49%.
  • 细胞内体积分数 (ICvf) 在30%的采样中显示L1的中位距离高达2.83%.

结论:

  • 在dMRI体积降低高达30%的情况下获得的DTI和NODDI指标与参考采样可比.
  • 使用三个shell (4,14,和32个方向) 的优化协议显著减少了获取时间.
  • 这些发现支持在大型临床研究中使用减少的dMRI协议来进行捆绑特异性扩散MRI分析.