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

11
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,...
11
Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

5.2K
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...
5.2K

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

Effect of Increased Image Matrix on Lung Nodule Volumetry in Chest CT: A Phantom Study.

Anticancer research·2026
Same author

Decreased Tissue Sodium Concentration in Suspected Prostate Cancer Detected by Internal-Reference <sup>23</sup>Na MRI: A Prospective Exploratory Study.

Diagnostics (Basel, Switzerland)·2026
Same author

Are you always working in the dark? The impact of limited daylight exposure on radiologists' health.

Insights into imaging·2026
Same author

The Impact of AI on Eye Gaze Patterns in Chest X-Ray Interpretation: An Eye Tracking Study of Novice and Expert Radiologists.

Investigative radiology·2026
Same author

Improved image quality and reduced acquisition time in brain MRI using deep learning-based reconstruction: A quantitative and subjective assessment compared to standard MPRAGE in 0.55 T MRI.

Magnetic resonance imaging·2026
Same author

Association Between AI-derived Thoracic Calcium Volume and Aortic Valve Calcification Quantified by Agatston Scoring.

In vivo (Athens, Greece)·2026
Same journal

MRI-based Predictors and Risk Constellations of Chronic Ankle Instability After Acute Lateral Ankle Sprain: A Multicenter Study.

Academic radiology·2026
Same journal

Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer using a Longitudinal US-based Stack-model.

Academic radiology·2026
Same journal

Evaluating the Impact of Embolization on Outcomes in Iliopsoas Hematomas: A Multicenter Retrospective Propensity-matched Study.

Academic radiology·2026
Same journal

Comparison of Iterative Metal Artifact Reduction Presets In Ultra-high-resolution Photon-counting CT Angiography of Patients with Total Knee Endoprosthesis.

Academic radiology·2026
Same journal

Deep Learning for Opportunistic Vertebral Fracture Detection on Routine Thoraco-abdominal Computed Tomography: A Systematic Review and Hierarchical Summary Receiver Operating Characteristic Meta-analysis of Patient-level Diagnostic Test Accuracy.

Academic radiology·2026
Same journal

"Where are They Now?": A Single Institution's 10-Years Experience with an Integrated Nuclear Radiology Fellowship.

Academic radiology·2026
查看所有相关文章

相关实验视频

Updated: Jul 11, 2025

Author Spotlight: Advancing Labor Management Through Electromyometrial Imaging for Understanding Uterine Contractions
08:07

Author Spotlight: Advancing Labor Management Through Electromyometrial Imaging for Understanding Uterine Contractions

Published on: May 26, 2023

1.2K

人工智能支持的自主子宫重建:在MRI中使用3D SPACE与代性排泄的首次应用.

Daniel Hausmann1, Aline Lerch2, Sebastian Hitziger3

  • 1Department of Radiology, Kantonsspital Baden, Im Ergel 1, Baden, 5404, Switzerland (D.H., A.L., M.F., M.G., K.H.); Department of Radiology and Nuclear Medicine, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany (D.H.).

Academic radiology
|November 4, 2023
PubMed
概括
此摘要是机器生成的。

一个新的AI算法成功地从3DMRI扫描中重建了子宫轴,匹配或超过了人类的性能. 这一创新有望简化子宫MRI分析和报告.

关键词:
三维空间是3D空间.算法算法是一种算法.人工智能的人工智能是人工智能.女性的骨盆是女性的骨盆.磁共振成像技术 磁共振成像技术

更多相关视频

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.5K
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

387

相关实验视频

Last Updated: Jul 11, 2025

Author Spotlight: Advancing Labor Management Through Electromyometrial Imaging for Understanding Uterine Contractions
08:07

Author Spotlight: Advancing Labor Management Through Electromyometrial Imaging for Understanding Uterine Contractions

Published on: May 26, 2023

1.2K
Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation
06:56

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Published on: January 7, 2021

2.5K
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

387

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 放射学 放射学是一门学科.

背景情况:

  • 子宫评估通常依赖于T2加权成像在直角平面.
  • 对子宫轴的准确重建对于全面分析至关重要.
  • 目前的方法可能会耗时或受到观察者之间的变化.

研究的目的:

  • 评估基于卷积神经网络 (CNN) 的算法,用于重建子宫轴.
  • 将人工智能算法的性能与子宫MRI分析中的人类专家进行比较.
  • 评估AI在提高子宫MRI报告效率方面的潜力.

主要方法:

  • 一项前性研究涉及50名接受子宫MRI的患者.
  • 在标准协议旁边获得三维空间序列的三角形方向.
  • 由实习生,经验丰富的放射科医生和原型AI软件进行子宫和腔腔轴的重建.
  • 使用利克特尺度和测量关键直径的重建的匿名评估.
  • 对观察者间协议的评估.

主要成果:

  • 在大多数子宫轴重建中,人工智能算法 (P) 的得分明显高于实习者 (T).
  • 在几个比较中,人工智能算法的性能与经验丰富的放射科医生 (E) 相当或优于.
  • 与人类重建相比,人工智能测量了明显更大的直径.
  • 在人类读者之间观察到中等到实质性的观察者间协议.

结论:

  • 人工智能算法在重建子宫轴方面表现出熟练,表现至少与人类专家一样好,如果不比人类专家更好.
  • 人工智能驱动的重建可能会促进工作流程,并提高子宫MRI报告的效率.
  • 这项技术有望提高诊断准确度,减少妇科成像报告时间.