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 Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

8.7K
Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
8.7K
Computed Tomography01:10

Computed Tomography

7.9K
Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
7.9K
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

2.8K
Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...
2.8K
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

257
DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
257
Deconvolution01:20

Deconvolution

524
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
524

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

TadA-mediated A-to-I mRNA editing rewires redox metabolism to promote dominance of epidemic <i><i>Klebsiella pneumoniae</i></i> clones.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

EnvZ/OmpR-driven cooperative behavior promotes cefiderocol resistance in a hanging-droplet evolution system.

Science advances·2026
Same author

Global spread and evolution of KPC-2 and NDM-1-producing Gram-negative bacteria.

Science China. Life sciences·2026
Same author

Control effects of joint grouting and precision blasting on blasting damage in deep rock masses.

Scientific reports·2025
Same author

Statistical prediction method of inclined shaft blasting fragmentation based on dynamic damage distribution in excavated rock mass.

Scientific reports·2025
Same author

Unraveling the evolution and global transmission of high level tigecycline resistance gene tet(X).

Environment international·2025

相关实验视频

Updated: Jan 8, 2026

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.9K

DLAO:一个以物理为基础的深度学习框架,用于光学连贯性断层扫描中的偏差校正.

Kaiwen Song, Peng Wang, Xinyu Guo

    Optics express
    |December 19, 2025
    PubMed
    概括

    这项研究引入了基于物理的深度学习自适应光学 (DLAO) 框架,以纠正光学偏差在光学连贯断层扫描 (OCT) 图像中的光学偏差. 通过提高分辨率和细节恢复,DLAO框架显著提高了图像质量.

    更多相关视频

    Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
    10:40

    Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

    Published on: August 12, 2025

    1.4K
    Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
    08:22

    Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

    Published on: January 12, 2022

    5.1K

    相关实验视频

    Last Updated: Jan 8, 2026

    Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
    11:21

    Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

    Published on: January 15, 2013

    11.9K
    Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography
    10:40

    Label-free, High-Resolution 3D Imaging and Machine Learning Analysis of Intestinal Organoids via Low-Coherence Holotomography

    Published on: August 12, 2025

    1.4K
    Application of Optical Coherence Tomography to a Mouse Model of Retinopathy
    08:22

    Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

    Published on: January 12, 2022

    5.1K

    科学领域:

    • 生物医学成像技术 生物医学成像技术
    • 光学工程是指光学工程.
    • 深度学习 (Deep Learning) 是一种深度学习.

    背景情况:

    • 光学连贯断层扫描 (OCT) 对于高分辨率,非侵入性生物医学成像至关重要.
    • 图像质量在OCT中经常因系统不完美和样本不均造成的光学偏差而降低.
    • 这些误差降低了空间分辨率,并掩盖了细节,限制了诊断和研究应用.

    研究的目的:

    • 开发一个有效的框架,以使用深度学习和自适应光学来纠正OCT图像中的复杂误差.
    • 提高因光学偏差而受到损害的OCT图像的空间分辨率和清晰度.

    主要方法:

    • 引入了一个基于物理的深度学习自适应光学 (DLAO) 框架.
    • 实现了一个伪点差函数 (伪PSF) 预处理步骤,以简化偏差校正到一个低维参数估计问题.
    • 设计了层层的自适应性渐进式注意网络 (LAPANet),具有多级特征融合和LAPA模块用于层次特征捕获.

    主要成果:

    • 与主流深度学习模型相比,DLAO框架,特别是LAPANet,在纠正误差方面表现出卓越的表现.
    • 实现了更高的峰值信号噪声比率 (PSNR) 和结构相似性指数 (SSIM) 分数.
    • 保持了高的推断效率,并在实验中表现出强度和概括能力.

    结论:

    • 拟议的DLAO框架有效地纠正了OCT图像中的复杂误差,显著提高了图像质量.
    • 拉帕网的协同设计精确地重建了关键的图像区域,并恢复了高频细节.
    • 该框架为改善在诊断和研究中的海外成像提供了实际价值.