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

5.3K
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...
5.3K

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

MicroKAN: Mapping human brain microstructure using diffusion MRI and adaptive nonlinear modeling.

NeuroImage·2026
Same author

I2I-Mamba: Multi-modal medical image synthesis via selective state space modeling.

IEEE transactions on bio-medical engineering·2026
Same author

Using U-Nets to Predict the Effects of Head Motion on Simulated Specific Absorption Rate for Ultra-High Field Magnetic Resonance Imaging With Parallel Transmission.

Magnetic resonance in medicine·2026
Same author

Meta-Entity Driven Triplet Mining for Aligning Medical Vision-Language Models.

IEEE journal of biomedical and health informatics·2026
Same author

Semi-supervision for clinical contrast-weighted image synthesis from magnetic resonance fingerprinting.

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

Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction.

IEEE transactions on medical imaging·2026

相关实验视频

Updated: Jul 25, 2025

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.2K

适应性扩散先验用于加速MRI重建.

Alper Güngör1, Salman Uh Dar2, Şaban Öztürk3

  • 1Department of Electrical and Electronics Engineering, Bilkent University, Ankara 06800, Turkey; National Magnetic Resonance Research Center (UMRAM), Bilkent University, Ankara 06800, Turkey; ASELSAN Research Center, Ankara 06200, Turkey.

Medical image analysis
|June 29, 2023
PubMed
概括

我们介绍AdaDiff,这是磁共振成像 (MRI) 重建之前的自适应扩散. 这种新的方法提高了图像质量和可靠性,超过了现有的技术,特别是当成像条件发生变化时.

关键词:
适应性的 适应性的扩散扩散是一种扩散.生成性的产生性.之前的图像 之前的图像这就是为什么MRI是MRI.重建重建的重建工作

更多相关视频

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

10.4K

相关实验视频

Last Updated: Jul 25, 2025

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia
09:59

A Magnetic Resonance Imaging Protocol for Stroke Onset Time Estimation in Permanent Cerebral Ischemia

Published on: September 16, 2017

14.2K
Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
15:48

Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging

Published on: December 15, 2014

22.5K
Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring
17:16

Registered Bioimaging of Nanomaterials for Diagnostic and Therapeutic Monitoring

Published on: December 9, 2010

10.4K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 图像重建 图像的重建

背景情况:

  • 对于MRI重建的深度学习模型,由于成像操作员的变化,通常会与一般化作斗争.
  • 无条件模型通过将图像先验与特定运营商的脱提供了更高的可靠性,扩散模型显示了高保真度.
  • 扩散模型中的静态图像先验可以在推断过程中导致低于最佳的性能.

研究的目的:

  • 在MRI重建之前开发了第一个自适应扩散,名为AdaDiff.
  • 为了提高MRI重建对域移动的性能和可靠性.
  • 提高MRI重建中的扩散模型的适应性.

主要方法:

  • 通过对抗映射在延长的反向扩散步骤上进行训练,AdaDiff利用了高效的扩散预先训练.
  • 采用了两阶段的重建过程:最初的快速扩散阶段,然后是适应阶段.
  • 适应阶段通过更新先前的数据来完善重建,以最大限度地减少数据一致性损失.

主要成果:

  • 与有条件和无条件方法相比,当AdaDiff在多对比脑MRI中受到域移位时,其表现优越.
  • 该方法在同一领域内实现了同等或优异的性能.
  • 与可变成像操作员相比,AdaDiff显示出可靠性和稳定性的显著改进.

结论:

  • AdaDiff代表了MRI重建的重大进步,提供了更好的适应性和性能.
  • 适应性扩散先验有效地解决了静态先验和域移动的局限性.
  • 这种方法有望在各种临床环境中实现更可靠,更高质量的MRI采集.