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

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

Simulation-Informed Evaluation of Microvascular Parameter Mapping for Diffusion MR Imaging of Solid Tumours.

Magnetic resonance in medicine·2026
Same author

Microstructural variation of hippocampal substructures across childhood and adolescence quantified with high-gradient diffusion MRI.

Communications biology·2026
Same author

Decoding gray matter, large-scale analysis of brain cell morphometry to inform microstructural modeling of diffusion MR signals.

Communications biology·2026
Same author

Histology-informed microstructural diffusion simulations for MRI cancer characterisation-the Histo-μSim framework.

Communications biology·2025
Same journal

Non-canonical amino acid incorporation enables minimally disruptive labeling of stress granule and TDP-43 proteinopathy.

eLife·2026
Same journal

Analysis of dendritic input currents during place field dynamics.

eLife·2026
Same journal

TopoMetry systematically learns and evaluates the latent geometry of single-cell data.

eLife·2026
Same journal

Navigating the path: Advice to physician-scientists on choosing a clinical specialty.

eLife·2026
Same journal

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex.

eLife·2026
Same journal

The exquisite mechanics of a tsetse bite.

eLife·2026
查看所有相关文章

相关实验视频

Updated: Jun 6, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K

通过使用深度学习的通用不确定性驱动推理引入量化成像的μGUIDE.

Maëliss Jallais1,2, Marco Palombo1,2

  • 1Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom.

eLife
|November 26, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了μGUIDE,这是一个贝叶斯框架,用于估计组织微观结构参数. 它有效地量化了扩散MRI数据中的不确定性,而不依赖获取约束.

关键词:
贝叶斯的推理 贝叶斯的推理扩散磁力共振成像 (MRI) 扩散人类 人类 人类 人类 人类 人类 人类微观结构成像成像技术神经科学 神经科学定量的MRI是指MRI的数量.基于模拟的推理推理.不确定性量化不确定性量化

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

2.9K

相关实验视频

Last Updated: Jun 6, 2025

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.5K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning
09:31

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Published on: April 28, 2022

2.9K

科学领域:

  • 生物物理学的生物物理.
  • 磁共振成像是一种磁共振成像技术.
  • 计算生物学 计算生物学

背景情况:

  • 从生物物理模型中估计组织微观结构参数是计算密集的.
  • 传统的贝叶斯方法通常需要特定的获取约束和特定模型的统计数据.

研究的目的:

  • 介绍 μGUIDE,一个一般的贝叶斯框架来估计组织微观结构参数的后部分布.
  • 为了证明μGUIDE在扩散权重磁共振成像 (dMRI) 中的应用.
  • 为了克服与传统贝叶斯式方法相关的计算和时间成本.

主要方法:

  • 使用一种新的深度学习架构来自动选择信号特征.
  • 采用基于模拟的推断来进行高效的后部分布采样.
  • 绕过依赖获取约束来定义特定模型的总结统计数据.

主要成果:

  • 与传统的贝叶斯方法相比,μGUIDE显著降低了计算和时间成本.
  • 该框架成功估计了微结构参数的后向分布.
  • 识别模型退化,量化参数不确定性和模两可.

结论:

  • μGUIDE为微结构参数估计提供了一个高效和可概括的贝叶斯框架.
  • 该方法通过提供可靠的不确定性量化来增强dMRI数据的分析.
  • μGUIDE 便于在没有限制性获取协议的情况下更深入地了解组织微观结构.