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

Optical Coherence Tomography Harmonization with Anatomy-Guided Latent Metric Schrödinger Bridges.

Advances in neural information processing systems·2026
Same author

Optical Coherence Tomography Harmonization via Dual Diffusion Implicit Bridges.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

Unsupervised OCT Image Interpolation Using Deformable Registration and generative models.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same author

An Unsupervised Approach for Artifact Severity Scoring in Multi-Contrast MR Images.

Proceedings of machine learning research·2026
Same author

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same author

Metabolomic Signatures of Brain Atrophy and Ibudilast Response in Progressive Multiple Sclerosis.

medRxiv : the preprint server for health sciences·2026

相关实验视频

Updated: Jun 5, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K

自主监督的超分辨率用于带有和没有切片间隙的无异型核磁共振图像.

Samuel W Remedios1,2, Shuo Han3, Lianrui Zuo4,5

  • 1Dept. of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA.

Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop)
|December 4, 2024
PubMed
概括

本研究介绍了一种自我监督的超分辨率 (SR) 方法,用于改进磁共振 (MR) 图像的厚片和间隙. 这种新技术提高了图像质量和体积分析的准确性,而不需要配对数据.

关键词:
这就是为什么MRI是MRI.深度学习是一种深度学习.自主监督的自我监督超级分辨率的超级分辨率

更多相关视频

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

276
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

相关实验视频

Last Updated: Jun 5, 2025

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench
11:38

Volume Segmentation and Analysis of Biological Materials Using SuRVoS Super-region Volume Segmentation Workbench

Published on: August 23, 2017

9.8K
Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods
05:07

Author Spotlight: Optimized Lung MRI Protocol with Computationally Efficient Reconstruction Methods

Published on: September 6, 2024

276
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
06:25

Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

Published on: February 12, 2014

8.4K

科学领域:

  • 医疗成像医学成像
  • 图像处理 图像处理
  • 人工智能的人工智能

背景情况:

  • 磁共振 (MR) 成像通常使用厚片来减少扫描时间和运动文物.
  • 在MRI图像中,厚切片和切片间的间隙可能会损害体积分析和3D重建的准确性.
  • 现有的超分辨率 (SR) 方法难以处理异构MR数据,特别是那些有切片间隙的方法,并且容易发生域转移.

研究的目的:

  • 开发一种自我监督的超分辨率 (SR) 技术,能够处理异构型MR图像,包括带有切片间隙的图像.
  • 通过提高MR图像分辨率来提高体积分析和3D方法的准确性.
  • 创建一个强大的SR方法,减轻数据驱动方法固有的领域转移问题.

主要方法:

  • 提出了一种新型的自主监督超分辨率 (SR) 算法,专门设计用于异构型MR图像.
  • 该方法有效地解决了厚切片和切片间隙的场景.
  • 将拟议的SR技术与两个开源数据集的现有方法进行了比较.

主要成果:

  • 自主监督的SR方法在信号恢复和下游任务执行方面都取得了显著的改进.
  • 该技术在具有或没有切片间隙的MR图像中被证明是有效的,其性能优于竞争方法.
  • 两组数据集的验证证实了拟议方法的稳定性和通用性.

结论:

  • 开发的自主监督SR技术为增强异构型MR图像提供了强大的解决方案,特别是那些有切片间隙的图像.
  • 这种方法提高了体积分析和3D重建的准确性,这对于临床应用至关重要.
  • 公开可用的代码有助于进一步研究和应用这种先进的图像处理技术.