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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

4.9K
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
4.9K

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

Time-Embedded Algorithm Unrolling for Computational MRI.

Advances in neural information processing systems·2026
Same author

Efficient Interleaved Multi-Band Outer Volume Suppression for Highly Accelerated Simultaneous Multi-Slice Imaging of the Heart.

Bioengineering (Basel, Switzerland)·2026
Same author

Edge Computing for Physics-Driven AI in Computational MRI: A Feasibility Study.

International Conference on Future Internet of Things and Cloud : FiCloud. International Conference on Future Internet of Things and Cloud·2026
Same author

Generative Model-Based Fusion for Improved Few-Shot Semantic Segmentation of Infrared Images.

IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision·2026
Same author

SPARSITY-DRIVEN PARALLEL IMAGING CONSISTENCY FOR IMPROVED SELF-SUPERVISED MRI RECONSTRUCTION.

Proceedings. International Conference on Image Processing·2026
Same author

Automated Tuning for Diffusion Inverse Problem Solvers without Generative Prior Retraining.

ArXiv·2025

相关实验视频

Updated: Jun 1, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.1K

强大的外部体积减去与深度学习的幽灵检测高度加速的实时动态MRI.

Merve Gülle1,2, Mehmet Akçakaya1,2

  • 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|January 21, 2025
PubMed
概括

本研究引入了一种深度学习 (DL) 方法,通过提高图像质量来增强实时MRI. 这种新的技术有效地在高加速度下重建图像,克服了当前方法的局限性.

关键词:
实时核磁共振成像深度学习是一种深度学习.鬼魂的文物 鬼魂的文物外体积减去外体积减去由物理驱动的DLL.

更多相关视频

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

12.8K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

442

相关实验视频

Last Updated: Jun 1, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.1K
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
10:06

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

12.8K
Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging
10:44

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

442

科学领域:

  • 医疗成像医学成像
  • 磁共振成像 (MRI) 是一种磁共振成像技术.
  • 人工智能的人工智能

背景情况:

  • 实时MRI对于心脏成像等动态过程至关重要,使得自由呼吸扫描成为可能.
  • 目前的技术由于加速率限制,难以实现时空分辨率.
  • 幽灵文物通常出现在低样本,时间间隔的MRI数据中.

研究的目的:

  • 开发一种深度学习 (DL) 技术,用于增强实时MRI.
  • 为了从低样本的MRI数据中改进静态外体积信号的估计.
  • 使用物理驱动的DL重建实时MR系列的个别时间框架.

主要方法:

  • 使用DL方法,从移动的,时间间隔的下面采样模式中估计静止的外部体积信号.
  • 该方法利用了由器官运动引起的幽灵艺术品的伪周期性.
  • 物理驱动的DL方法用于在信号减去后的个别时间框架重建.

主要成果:

  • 拟议的DL技术显著提高了实时MRI中的图像质量.
  • 在高加速度时观察到更好的性能,而传统方法却失败了.
  • 实现了有效的文物抑制和提高分辨率.

结论:

  • 开发的DL方法为高质量的实时MRI提供了一个有前途的解决方案.
  • 这种技术解决了当前方法在实现高时空分辨率方面的局限性.
  • 这种方法可以改善动态成像,特别是在诸如心脏成像等具有挑战性的场景中.