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 VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

32
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
32

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

Association of sleep duration with Alzheimer's disease and cognition.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same author

A Conformable CMOS Ultrasound System for Point-of-Care Imaging.

medRxiv : the preprint server for health sciences·2026
Same author

Brain Iron in Nucleus Accumbens and Cognitive Function in Preeclampsia.

Hypertension (Dallas, Tex. : 1979)·2026
Same author

Environmental exposure to rare earth elements and respiratory health: an emerging public health concern.

Frontiers in public health·2026
Same author

Multi-Amplitude Modulation Frequency in Harmonic Motion Imaging Guided Focused Ultrasound (HMIgFUS) on Ablated Lesion Characterization and Monitoring in an in Vivo Breast Cancer Mouse Model.

IEEE transactions on bio-medical engineering·2026
Same author

Deep learning-driven MRI segmentation of choroid plexus volume: a novel biomarker for cognitive impairment in type 2 diabetes mellitus.

Japanese journal of radiology·2026

相关实验视频

Updated: Jul 26, 2025

Monitoring the Wall Mechanics During Stent Deployment in a Vessel
08:28

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Published on: May 8, 2012

9.3K

基于深度学习的无监督移位估计用于血管弹性成像应用程序.

Grigorios M Karageorgos1, Pengcheng Liang1, Nima Mobadersany2

  • 1Biomedical Engineering Department, Columbia University, New York, NY, United States of America.

Physics in medicine and biology
|June 22, 2023
PubMed
概括

一种新的深度学习方法增强了基于超声波的动脉壁位移估计,以更好地评估心血管健康. 这种技术提高了绘制动脉硬度和脉冲波速度的准确性.

关键词:
动脉壁的移位动脉壁的移位冠状动脉疾病 冠状动脉疾病深度学习是一种深度学习.神经网络的神经网络的神经网络血管弹性成像 血管弹性成像

更多相关视频

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.5K
Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish
09:15

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Published on: February 6, 2020

5.0K

相关实验视频

Last Updated: Jul 26, 2025

Monitoring the Wall Mechanics During Stent Deployment in a Vessel
08:28

Monitoring the Wall Mechanics During Stent Deployment in a Vessel

Published on: May 8, 2012

9.3K
Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens
09:29

Using Digital Image Correlation to Characterize Local Strains on Vascular Tissue Specimens

Published on: January 24, 2016

9.5K
Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish
09:15

Displacement Analysis of Myocardial Mechanical Deformation DIAMOND Reveals Segmental Heterogeneity of Cardiac Function in Embryonic Zebrafish

Published on: February 6, 2020

5.0K

科学领域:

  • 生物医学工程 生物医学工程
  • 医疗成像医学成像
  • 心血管研究研究心血管研究

背景情况:

  • 动脉壁硬度是心血管健康的关键指标.
  • 超声波弹性成像为评估动脉硬性提供了一种非侵入性方法.
  • 精确的动脉壁位移估计对于这些成像技术至关重要.

研究的目的:

  • 开发一种无监督的深度学习方法,以改进动脉壁位移估计.
  • 为了提高动脉硬度和脉冲波速度 (PWV) 映射的质量.
  • 为了验证该方法使用幻影实验和体内体验的人类动脉.

主要方法:

  • 采用了一种无监督的深度学习模型,该模型是从图像注册技术中改造出来的.
  • 模型使用超声波射频信号和B模式图像进行训练,具有不同的损失函数,包括平均平方误差 (MSE).
  • 基于信号与噪声比 (SNR),对比与噪声比 (CNR) 和脉冲波速度 (PWV) 的准确性来评估性能.

主要成果:

  • 在MSE的B模式图像上的训练产生了最高的SNR (30.36 ± 1.14 dB).
  • 使用MSE对射频信号的训练导致最高的CNR (32.84±1.89dB).
  • 经过射频训练的模型准确地绘制了PWV,其平均相对误差 (MREPWV) 在幻体中为3.32 ± 1.80%,在体内为3.86 ± 2.69%,R值分别为0.97和0.95.

结论:

  • 提出的深度学习方法显著改善了动脉壁位移估计.
  • 该方法在幻影研究和体内研究中都显示出高准确性.
  • 这种技术有望推进血管弹性成像和心血管评估.