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

4.8K
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...
4.8K

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

Deep learning-based Desikan-Killiany parcellation of the brain using diffusion MRI.

Scientific reports·2026
Same author

Deep-learning-based spectral motion artifact correction on photon-counting cardiac CT images.

Physics in medicine and biology·2026
Same author

Alport Syndrome is a Partial Tubulointerstitial Disease of the Kidney.

Kidney international reports·2026
Same author

Spatial transcriptomics expression prediction from histopathology based on cross-modal mask reconstruction and contrastive learning.

Medical image analysis·2025
Same author

Continual learning in medical image analysis: A comprehensive review of recent advancements and future prospects.

Medical image analysis·2025
Same author

Domain-incremental white blood cell classification with privacy-aware continual learning.

Scientific reports·2025

相关实验视频

Updated: May 9, 2025

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals
07:57

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals

Published on: April 25, 2017

8.3K

在医学成像中使用物理启发的生成模型.

Dennis Hein1,2, Afshin Bozorgpour3, Dorit Merhof3

  • 1Department of Physics, KTH Royal Institute of Technology, Stockholm, Sweden.

Annual review of biomedical engineering
|May 1, 2025
PubMed
概括

灵感来自物理学的生成模型,包括扩散和波桑流模型,正在彻底改变医学成像. 这篇评论强调了它们在重建,生成和分析中的应用,为未来的进步铺平了道路.

关键词:
贝叶斯定理 贝叶斯定理在PFGM++++中使用.波桑流量生成模型的模型一致性模型的一致性模型扩散模型的扩散模型.图像分析图像分析图像重建 图像重建图像/数据合成医学成像医学成像由物理启发的生成模型.

更多相关视频

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
11:36

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

Published on: February 9, 2022

2.7K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.7K

相关实验视频

Last Updated: May 9, 2025

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals
07:57

Scaled Anatomical Model Creation of Biomedical Tomographic Imaging Data and Associated Labels for Subsequent Sub-surface Laser Engraving SSLE of Glass Crystals

Published on: April 25, 2017

8.3K
Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
11:36

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

Published on: February 9, 2022

2.7K
Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

1.7K

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算物理 计算物理

背景情况:

  • 生成模型 (GMs) 在医学成像中越来越重要.
  • 灵感来自物理学的GM,如扩散和波桑流模型,提供了增强的贝叶斯方法.
  • 这些模型显示了改善各种医学成像任务的重大前景.

研究的目的:

  • 审查物理启发的生成模型在医学成像中的作用.
  • 检查这些模型的准确性,稳定性和加速性.
  • 概述这一领域对生成方法的未来研究方向.

主要方法:

  • 对无声扩散概率模型,基于分数的扩散模型和Poisson流生成模型 (PFGM++) 的审查.
  • 分析模型性能,重点关注准确性,稳定性和加速性.
  • 探索医学图像重建,生成和分析中的应用.

主要成果:

  • 灵感来自物理学的GM在医学成像应用中显示出显著的实用性.
  • 扩散和波桑流量模型等关键模型显示了增强性能的前景.
  • 该审查确定了这些先进的GM的当前能力和局限性.

结论:

  • 灵感来自物理学的转基因对医学成像具有变革性,增强了贝叶斯方法.
  • 未来的研究应该专注于统一这些模型,将它们与视觉语言模型集成,并探索新的应用.
  • 本综述提供了一个及时的概述,以利用物理驱动的GM在医学成像中的潜力.