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

Imaging Studies IV: Magnetic Resonance Imaging01:27

Imaging Studies IV: Magnetic Resonance Imaging

Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...

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

Correction: Feasibility of multifrequency MR elastography of the seminal vesicles in healthy men, benign prostatic hyperplasia, and prostate cancer.

European radiology experimental·2026
Same author

Crossover frequency as a model-independent viscoelastic constant for soft tissue biomechanics.

Acta biomaterialia·2026
Same author

Radiofrequency ablation produces stiffer atrial lesions than pulsed-field ablation: in vivo and ex vivo MR elastography in pigs.

Heart rhythm·2026
Same author

Brain MR Elastography Metrics Associated with Alterations in Learning and Memory in People with HIV.

Research square·2026
Same author

Multifrequency tabletop magnetic resonance elastography for ex-vivo characterization of murine intestinal tissue biomechanics.

Journal of the mechanical behavior of biomedical materials·2026
Same author

Stiffness and Tissue Viscosity in a Cerebral Neoplasm Measured by Preoperative Multifrequency Magnetic Resonance Elastography (MRE) Guide the Differential Diagnosis of Brain Tumors by Ruling Out Glioma.

Case reports in medicine·2026

相关实验视频

Updated: Jun 20, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K

使用U-Nets对MR弹性图的肝脏和脏进行自动细分.

Noah Jaitner1, Jakob Ludwig1, Tom Meyer1

  • 1Department of Radiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, 12203, Berlin, Germany.

Scientific reports
|March 29, 2025
PubMed
概括

使用U-Nets在多频磁共振弹性学 (MRE) 大小图像中的自动化肝脏和脏细分能够准确量化剪切波速度 (SWS). 预训练和训练的U-Nets都表现出了出色的性能,与手动细分相当.

更多相关视频

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

996

相关实验视频

Last Updated: Jun 20, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

3.6K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

3.9K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

996

科学领域:

  • 医疗成像医学成像
  • 人工智能在医学中的应用
  • 生物物理学的生物物理.

背景情况:

  • 磁共振弹性图 (MRE) 是一种非侵入性技术,用于评估组织硬度.
  • 肝脏和脏等器官的准确细分对于量化剪切波速度 (SWS) 至关重要.
  • 手动细分是耗时的,容易引起观察者之间的变化.

研究的目的:

  • 为了比较预训练和训练的2D和3DU-Net模型,用于在多频率MRE大小图像中自动化肝脏和脏细分.
  • 用这些细分模型评估SWS自动定量化的可行性.
  • 为了评估U-Nets的性能与手动细分使用相关性,ICC和Dice分数.

主要方法:

  • 72名健康参与者接受了1.5T或3T的多频率MRE.
  • 对肝脏和脏有兴趣的体积 (VOI) 进行了手动细分 (地面真相) 和自动使用预训练和训练的2D和3DU-Nets在MRE大小图像上.
  • 使用相关性分析,类内相关系数 (ICCs) 和VOI和SWS值的Dice分数来评估绩效.

主要成果:

  • 在VOI和SWS值的地面真相和U-Net细分之间没有发现统计学上显著的差异 (p ≥0.95).
  • 观察到强烈的正相关性 (肝脏R=0.99,脏R=0.81-0.84) 和优异的一致性 (肝脏ICC=0.99,脏ICC=0.90-0.92).
  • 2D U-Net获得了略高的子分数 (肝脏:0.95,脏:0.90),表明了优异的细分性能,U-Net模型之间的差异最小.

结论:

  • 在MRE大小图像上使用2D和3DU-Nets的自动肝脏和脏细分是非常可行的和准确的.
  • 利用MRE大小图像中的解剖信息,可以完全自动量化MRE参数.
  • 这种方法为在临床环境中高效可靠的SWS量化提供了一个有希望的解决方案.