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

5.0K
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...
5.0K

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

Federated Pseudo Modality Generation for Incomplete Multi-Modal MRI Reconstruction.

IEEE journal of biomedical and health informatics·2025
Same author

Self-supervised multi-modality learning for multi-label skin lesion classification.

Computer methods and programs in biomedicine·2025
Same author

Modality-Aware Distillation Network for Microvascular Invasion Prediction of Hepatocellar Carcinoma From MRI Images.

IEEE transactions on bio-medical engineering·2025
Same author

MHFNet: A Multimodal Hybrid-Embedding Fusion Network for Automatic Sleep Staging.

IEEE journal of biomedical and health informatics·2025
Same author

Enhancing Medical Vision-Language Contrastive Learning via Inter-Matching Relation Modeling.

IEEE transactions on medical imaging·2025
Same author

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society·2025

相关实验视频

Updated: Jun 16, 2025

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra
05:14

Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

Published on: September 8, 2021

3.3K

显式异常提取无监督的运动人工物 减少磁共振成像中的显式异常提取

Yusheng Zhou, Hao Li, Jianan Liu

    IEEE journal of biomedical and health informatics
    |August 16, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一种新的无监督深度学习网络 (UNAEN),用于在MRI中减少运动工件. 通过使用未配对图像,UNAEN有效地减少了文物,提高了诊断准确度和图像导向治疗.

    更多相关视频

    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
    11:00

    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

    Published on: March 19, 2021

    4.4K
    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
    09:30

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

    Published on: December 18, 2016

    19.5K

    相关实验视频

    Last Updated: Jun 16, 2025

    Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra
    05:14

    Standardized Data Acquisition for Neuromelanin-Sensitive Magnetic Resonance Imaging of the Substantia Nigra

    Published on: September 8, 2021

    3.3K
    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
    11:00

    Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

    Published on: March 19, 2021

    4.4K
    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease
    09:30

    Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

    Published on: December 18, 2016

    19.5K

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 运动工件显著降低了磁共振成像 (MRI) 的质量,阻碍了诊断和图像导向治疗.
    • 监督深度学习方法的运动工件减少 (MAR) 需要配对损坏和无工件图像,这是很难获得的.
    • 这种限制限制了监督MAR技术在临床环境中的实际应用.

    研究的目的:

    • 提出一个新的无监督深度学习网络,UNAEN,用于MRI的运动器件减少.
    • 为了使MAR能够使用未配对的损坏的和没有文物的MR图像,克服监督方法的局限性.
    • 提高MRI扫描的质量,以提高诊断准确度和图像导向治疗.

    主要方法:

    • 开发了一个未经监督的异常提取网络 (UNAEN),它运行在未配对的MRI数据集上.
    • 实现了文物提取器,以识别和隔离文物地图从损坏的MRI图像.
    • 使用重建器从工件减少的图像中恢复图像质量.

    主要成果:

    • 与最先进的MAR方法相比,UNAEN在各种公共MRI数据集上表现出卓越的性能.
    • 定量评估证实了UNAEN在减少运动工件方面的有效性.
    • 视觉评估显示,在UNAEN处理的图像中,残留物件显著减少.

    结论:

    • 乌纳恩提供了一个有前途的无监督解决方案,用于在MRI中减少运动工件.
    • 该网络能够处理未配对数据的能力使其适用于现实世界的临床应用.
    • 乌纳恩有潜力提高诊断准确度,并促进先进的图像引导疗法.