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

State Space Representation01:27

State Space Representation

631
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
631
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

14.7K
Super-resolution fluorescence microscopy (SRFM) provides a better resolution than conventional fluorescence microscopy by reducing the point spread function (PSF). PSF is the light intensity distribution from a point that causes it to appear blurred. Due to PSF, each fluorescing point appears bigger than its actual size, and it is the PSF interference of nearby fluorophores that causes the blurred image. Various approaches to achieving higher resolution through SRFM have recently been...
14.7K

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

Accuracy of Key Sonographic Markers for Juxta-Articular Fractures: A Prospective Study Using Explainable Machine Learning.

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine·2026
Same author

LMGDM: A Lesion-aware Mutual Guidance Diffusion Model with attenuation prior constraint for self-attenuation correction of whole-body PET.

Medical image analysis·2026
Same author

All-optical photoacoustic transduction and detection with 2D semiconductors for <i>in situ</i> evaluation of integrated chip buried interfaces.

Nanoscale·2026
Same author

Enhanced vertebrae localization in CT volumes: a two-stage deep learning framework.

BMC medical imaging·2026
Same author

Imaging foundation model for universal enhancement of non-ideal measurement CT.

Nature communications·2026
Same author

A Two-Stage Contrastive Learning Framework Grounded in Label-Specific Features for Low-Frequency Labels in Chest X-Ray Multi-Label Classification.

Bioengineering (Basel, Switzerland)·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
Same journal

Cross-subject fMRI-to-Image with Visual-cortex 2D Representation and Pre-Training.

IEEE journal of biomedical and health informatics·2026
Same journal

PGCASurv: A Prior-Guided Cross-Attention Framework for Dynamic Survival Model with Longitudinal Data.

IEEE journal of biomedical and health informatics·2026
查看所有相关文章

相关实验视频

Updated: Mar 1, 2026

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
07:26

Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

Published on: March 28, 2025

1.2K

USRMamba:用于超声超分辨率的自适应路由引导状态空间模型.

Tao Wang, Zihan Zhou, Chufeng Jin

    IEEE journal of biomedical and health informatics
    |February 27, 2026
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了USRMamba,一种基于Mamba的新方法,用于增强超声波 (美国) 图像分辨率. 通过解决衍射极限和噪声,USRMamba显著提高了图像质量和诊断准确性.

    更多相关视频

    A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents
    08:02

    A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents

    Published on: July 16, 2020

    5.5K
    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

    8.9K

    相关实验视频

    Last Updated: Mar 1, 2026

    Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy
    07:26

    Two-Dimensional Super-Resolution Visualization of Rat Brain Microvasculature Using Ultrasound Localization Microscopy

    Published on: March 28, 2025

    1.2K
    A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents
    08:02

    A High-Throughput Image-Guided Stereotactic Neuronavigation and Focused Ultrasound System for Blood-Brain Barrier Opening in Rodents

    Published on: July 16, 2020

    5.5K
    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
    06:25

    Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform

    Published on: February 12, 2014

    8.9K

    科学领域:

    • 医疗成像医学成像
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 超声波 (US) 影像分辨率受到声波衍射和传感器密度的限制,影响临床诊断.
    • 超分辨率 (SR) 重建为改善美国图像质量的系统升级提供了具有成本效益的替代方案.
    • 现有的SR方法与组织的复杂声学特性作斗争,阻碍了统一模型的开发.

    研究的目的:

    • 开创一种新的基于Mamba的单一美国图像SR方法,命名为USRMamba.
    • 通过先进的SR重建,提高超声波图像的真实性和诊断效用.
    • 克服当前超声波成像SR技术的局限性.

    主要方法:

    • 开发了USRMamba,一种基于Mamba的SR方法,用于单个超声波图像.
    • 引入了一种增强的转换组合模块 (ETCM) 用于多级特征提取,解决高频损失和斑点噪声.
    • 提出了使用自适应路由的自适应Top-k提示模块 (ATPM),以减轻因衰减引起的模糊区域干扰.
    • 集成了一个频道注意模块 (FCAM),用于并行频率空间域的重建.

    主要成果:

    • 与现有方法相比,USRMamba在各种美国数据集上表现出优异的性能.
    • 该方法在 ×2 尺度因子下实现了比最先进的 (SOTA) 方法高出1.31dB的平均PSNR.
    • 定性和定量实验验证了USRMamba在优化美国图像SR重建方面的有效性.

    结论:

    • USRMamba代表了一种革命性的方法,用于单一的美国图像SR重建.
    • 拟议的方法有效地提高了超声波成像中的图像质量和细节重建.
    • 通过优异的超声波图像分辨率,USRMamba显示了改善临床诊断的巨大潜力.