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

Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

13.6K
The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
13.6K

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

Enantioselective Radical Ring-Opening Cyanation of Oxime Esters by Dual Photoredox and Copper Catalysis.

Organic letters·2019
Same author

ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING.

Proceedings. IEEE International Symposium on Biomedical Imaging·2019
Same author

Technical note: Development and application of KASP assays for rapid screening of 8 genetic defects in Holstein cattle.

Journal of dairy science·2019
Same author

Sesquiterpenes and diterpenes from Euphorbia thymifolia.

Fitoterapia·2019
Same author

Glechomanamides A-C, Germacrane Sesquiterpenoids with an Unusual Δ<sup>8</sup>-7,12-Lactam Moiety from <i>Salvia scapiformis</i> and Their Antiangiogenic Activity.

Journal of natural products·2019
Same author

Parameter optimization framework on wave gradients of Wave-CAIPI imaging.

Magnetic resonance in medicine·2019

相关实验视频

Updated: May 2, 2026

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
07:21

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

14.4K

适应序列贝叶斯代式学习用于对心肌图像序列的心肌运动估计.

Shuxin Zhuang, Heye Zhang, Dong Liang

    IEEE transactions on medical imaging
    |August 18, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了自适应序列贝叶斯代式学习,用于在心脏成像中准确地估计左心室心肌运动. 这种新的方法有效地处理强度变化,改善了通过各种方式评估心脏功能.

    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
    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
    08:10

    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

    Published on: July 20, 2022

    1.8K

    相关实验视频

    Last Updated: May 2, 2026

    Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
    07:21

    Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

    Published on: February 12, 2011

    14.4K
    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
    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
    08:10

    Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation

    Published on: July 20, 2022

    1.8K

    科学领域:

    • 医疗成像医学成像
    • 生物医学工程 生物医学工程
    • 计算心脏病学 计算心脏病学

    背景情况:

    • 精确的运动估计左心室心肌对于评估心脏功能至关重要.
    • 心脏图像序列经常表现出强度变化,这给精确的心肌运动估计带来了挑战.
    • 这些与成像相关的干扰在不同的心脏成像模式中普遍存在.

    研究的目的:

    • 开发一种先进的方法,用于在心脏成像中进行强大的心肌运动估计.
    • 为应对心脏图像序列强度变化引起的不确定干扰的挑战.
    • 通过增强的运动估计,提高心脏功能评估的准确性.

    主要方法:

    • 拟议的自适应序列贝叶斯代式学习,以克服运动估计的挑战.
    • 应用适应性结构推断到状态过渡和观察不确定性下的复杂心肌运动.
    • 建立了用于隐性表示的等级结构重复性和用于运动相关性的链结构映射.

    主要成果:

    • 在1270名患者的广泛数据集 (美国,CMR,TMR) 上证明了拟议方法的有效性.
    • 与八种最先进的运动估计方法相比,展示了更高的性能.
    • 在不同心脏成像模式中验证了该方法的稳定性.

    结论:

    • 适应序列贝叶斯代学习为心肌运动估计提供了强大的解决方案.
    • 该方法有效地处理心脏成像中的强度变化和复杂的运动模式.
    • 这种方法显著提高了对心脏功能的评估.