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

Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

349
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
349
Curvilinear Motion: Rectangular Components01:23

Curvilinear Motion: Rectangular Components

447
Curvilinear motion characterizes the movement of a particle or object along a curved path, notably evident when envisioning a car navigating a winding road. If the car starts at point A, its position vector is established within a fixed frame of reference, where the ratio of the position vector to its magnitude signifies the unit vector pointing in the position vector's direction.
As the car advances, its position evolves over time. Quantifying the car's velocity involves computing the...
447
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

333
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
333

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

Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering.

IEEE transactions on visualization and computer graphics·2026
Same author

Topology Assisted Clustering of Temporal fMRI Brain Networks With Use-Case in Mitigating Non-Neural Multi-Site Variability.

IEEE access : practical innovations, open solutions·2026
Same author

UALCAN Mobile, an app for cancer proteogenomic data analysis.

Research square·2025
Same author

UALCAN Mobile, an app for cancer proteogenomic data analysis.

bioRxiv : the preprint server for biology·2025
Same author

Diffusion probabilistic generative models for accelerated, in-NICU permanent magnet neonatal MRI.

Magnetic resonance in medicine·2025
Same author

Robust multi-coil MRI reconstruction via self-supervised denoising.

Magnetic resonance in medicine·2025
Same journal

A Comparison of Tissue Property Values Estimated Using Conventional Cardiac MRF and MT-Cardiac MRF.

Magnetic resonance in medicine·2026
Same journal

Dependence of the Extra-Cellular Diffusion Coefficient on the Fractions of Neurites and Cell Bodies in Gray Matter.

Magnetic resonance in medicine·2026
Same journal

Triple-Pulse <sup>23</sup>Na MRI Sequence (TriNa) for Simultaneous Acquisition of Spin-Density-Weighted and Fluid-Attenuated Images.

Magnetic resonance in medicine·2026
Same journal

Evaluation of Phantom Doping Materials in Quantitative Susceptibility Mapping.

Magnetic resonance in medicine·2026
Same journal

Design of an 8-Channel Transmit 32-Channel Receive 11.7T Head Coil and Evaluation of SNR Gains.

Magnetic resonance in medicine·2026
Same journal

The Potential for Absolute Temperature Imaging Based on Brain Metabolites Using an FID-Shifting Approach in Gradient Echo Planar Spectroscopic Imaging (GREPSI).

Magnetic resonance in medicine·2026
查看所有相关文章

相关实验视频

Updated: Jun 27, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.4K

使用深度生成扩散模型进行加速运动校正.

Brett Levac1, Sidharth Kumar1, Ajil Jalal2

  • 1Chandra Family Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, Texas, USA.

Magnetic resonance in medicine
|April 30, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新方法,使用深度生成扩散模型来重建清晰的MRI图像,尽管主体运动和数据加速. 该技术有效地在没有外部信号的情况下纠正运动工件,提高图像质量.

关键词:
核磁共振成像 (MRI) 重建的重建深度生成的扩散模型.深度学习是一种深度学习.运动校正,运动校正.

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
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.3K

相关实验视频

Last Updated: Jun 27, 2025

Sample Drift Correction Following 4D Confocal Time-lapse Imaging
10:04

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

Published on: April 12, 2014

16.4K
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
Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects
10:16

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

Published on: February 8, 2014

12.3K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算科学 计算科学

背景情况:

  • 加速磁共振成像 (MRI) 对于缩短扫描时间至关重要.
  • 在MRI过程中对象的运动引入了文物,降低了图像质量,并使重建复杂化.
  • 现有的运动校正方法经常与加速数据作斗争,或需要外部参考信号.

研究的目的:

  • 开发一种用于加速MRI图像重建的强大方法.
  • 为了同时纠正对象运动和前进模型的不完美.
  • 为了解决运动损坏的MRI数据中的错误的反向问题.

主要方法:

  • 采用使用深度生成扩散模型的贝叶斯框架.
  • 该方法共同估计无运动图像和刚性运动参数.
  • 重建是在部分采样,运动损坏的2D k空间数据上进行的.

主要成果:

  • 成功地从加速的二维卡特西安和非卡特西安MRI扫描中重建了无运动图像.
  • 在不依赖外部参考信号的情况下,证明了有效的运动校正.
  • 在模拟和前性获得的加速数据上,超越了现有的校正技术.

结论:

  • 开发了一种灵活的框架,用于加速MRI中的回顾性运动校正.
  • 拟议的方法利用深度生成扩散模型进行增强的重建.
  • 潜在的应用扩展到纠正其他前模型腐败在MRI.