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

Brain Imaging01:14

Brain Imaging

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 Stimulation (TMS).

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

Restriction-Weighted Q-Space Trajectory Imaging (ResQ): Toward Mapping Diffusion-Time Effects With Tensor-Valued Diffusion Encoding in Human Prostate Cancer Xenografts.

NMR in biomedicine·2026
Same author

The impact of pain in children and adolescents with cerebral palsy: A daily diary study.

Acta psychologica·2026
Same author

Concomitant Gradient Effects Across Field Strengths and Gradient Amplitudes: Improved Estimation of Errors and Correction of Concomitant Dephasing and Diffusion Weighting.

Magnetic resonance in medicine·2026
Same author

Microstructure imaging of prostate cancer by diffusion MRI.

Magma (New York, N.Y.)·2026
Same author

The Role of Dendritic Spines in Water Exchange Measurements With Diffusion MRI: Double Diffusion Encoding and Free-Waveform MRI.

NMR in biomedicine·2026
Same author

Tensor-Valued Diffusion MRI for Microstructural Assessment During Stereotactic Radiotherapy of Brain Metastases: A Feasibility Study.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Differential deficits in pattern- versus flash-visual evoked potentials in schizophrenia: relationship to subcortical visual systems, pulvinar nucleus and cognition.

Frontiers in neuroimaging·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
查看所有相关文章

相关实验视频

Updated: Jun 26, 2026

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

球形卷积神经网络可以从扩散MRI数据中改善大脑微观结构的估计.

Leevi Kerkelä1, Kiran Seunarine1,2, Filip Szczepankiewicz3

  • 1UCL Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.

Frontiers in neuroimaging
|March 29, 2024
PubMed
概括
此摘要是机器生成的。

旋转不变的球状卷积神经网络增强了扩散MRI中的微结构参数估计. 这种机器学习方法提高了准确性,并减少了与传统脑组织分析方法相比的差异.

关键词:
这就是为什么MRI是MRI.扩散磁共振成像技术的使用.几何深度学习的几何深度学习微观结构的微观结构球形卷积神经网络是一个球形卷积神经网络.

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
Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
13:26

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

12.3K

相关实验视频

Last Updated: Jun 26, 2026

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
Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
13:26

Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

Published on: August 11, 2016

12.3K

科学领域:

  • 神经成像是一种神经成像.
  • 生物物理学的生物物理.
  • 机器学习 机器学习

背景情况:

  • 扩散磁共振成像 (dMRI) 测量了大脑组织中的水扩散.
  • 从dMRI信号中估计微结构性质是一个复杂的反向问题.
  • 机器学习为改善基于dMRI的估计提供了潜在的解决方案.

研究的目的:

  • 评估旋转不变的球状卷积神经网络 (RISCNNs) 在dMRI中微观结构参数估计的有效性.
  • 为了比较RISCNNs的性能与已建立的方法,如球体平均技术 (SMT) 和多层感知子 (MLPs).

主要方法:

  • 在模拟的噪音dMRI数据上训练了一个RISCNN,以预测基准真实性的微观结构参数.
  • 将训练有素的网络应用于临床dMRI数据,以生成微结构参数图.
  • 使用双隔间和三隔间模型进行参数估计.

主要成果:

  • 与SMT和MLP相比,RISCNN表现出优越的性能.
  • 实现了比SMT更高的预测准确度.
  • 与MLP相比,其旋转变异性较小.
  • 成功估计了三部分模型的参数,包括明显的神经 soma 密度.

结论:

  • 在dMRI数据的微结构参数估计中,RISCNNs代表了显著的进步.
  • 开发的网络和管道可以将其推广到各种高斯分隔式模型中.
  • 这种方法有望为dMRI的增强的临床和科学应用提供希望.