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

Deconvolution01:20

Deconvolution

133
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...
133
Properties of DTFT II01:24

Properties of DTFT II

179
In the study of discrete-time signal processing, understanding the properties of the Discrete-Time Fourier Transform (DTFT) is crucial for analyzing and manipulating signals in the frequency domain. Several properties, including frequency differentiation, convolution, accumulation, and Parseval's relation, offer powerful tools for signal analysis.
The frequency differentiation property is illustrated by considering a DTFT pair and differentiating both sides with respect to ω.
179
Super-resolution Fluorescence Microscopy01:37

Super-resolution Fluorescence Microscopy

6.9K
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...
6.9K

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

IoT-integrated rGO@Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene-based ammonia sensors: a DFT mechanistic insight for real-time monitoring.

Nanoscale·2026
Same author

HyCANet: a hybrid convolutional-attention network with cross-attention fusion for lung cancer histopathological classification.

Scientific reports·2026
Same author

Time-of-flight fluorescence depth mapping using a spatiotemporal deep learning model.

Journal of biomedical optics·2026
Same author

Systematic validation of time-resolved diffuse optical simulators via non-contact spad-based measurements.

Physics in medicine and biology·2026
Same author

Fluorescence-lifetime optical electrophysiology in contracting cardiomyocytes.

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

Accelerated SPAD-Based Diffuse Optical Tomography With Data-Driven View Optimization.

Journal of biophotonics·2026

相关实验视频

Updated: Jun 7, 2025

Direct Imaging of Laser-driven Ultrafast Molecular Rotation
10:52

Direct Imaging of Laser-driven Ultrafast Molecular Rotation

Published on: February 4, 2017

9.7K

基于深度学习的时间解卷用于光子飞行时间分布检索.

Vikas Pandey, Ismail Erbas, Xavier Michalet

    Optics letters
    |November 15, 2024
    PubMed
    概括

    我们开发了一个深度学习模型,以准确地解构时间解析的光终身成像数据. 这种方法简化了复杂的计算,改善了生物医学成像中的光子飞行时间分析.

    科学领域:

    • 生物医学光学 生物医学光学
    • 计算成像技术的成像
    • 机器学习在科学中的应用

    背景情况:

    • 飞行时间 (ToF) 获取对于生物医学应用至关重要.
    • 现有的仪器响应函数 (IRF) 解卷的方法是计算密集的,需要规范化.
    • 准确的解卷对于解释时间解析的实验数据至关重要.

    研究的目的:

    • 引入一种新的深度学习模型,用于光终身成像 (FLI) 中的解卷.
    • 通过解决来自IRF的扭曲来检索真正的光子ToF分布.
    • 为传统的解卷技术提供一个计算效率高,准确的替代方案.

    主要方法:

    • 使用模拟的FLI数据开发和训练一个深度学习模型.
    • 用模拟数据验证模型,以评估其恢复真实ToF分布的能力.
    • 使用不同IRF的体外时间解析成像方式进行实验验验证.
    • 通过体内临床前研究进行进一步验证.

    主要成果:

    • 深度学习模型成功地对模拟的FLI数据进行了解卷.
    • 在不同的体外成像模式和各种IRF中,证明了强大的性能.
    • 该模型在体内临床前研究中被证明是有效的.

    更多相关视频

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    2.8K
    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    16.3K

    相关实验视频

    Last Updated: Jun 7, 2025

    Direct Imaging of Laser-driven Ultrafast Molecular Rotation
    10:52

    Direct Imaging of Laser-driven Ultrafast Molecular Rotation

    Published on: February 4, 2017

    9.7K
    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations
    13:13

    Time-Lapse Imaging of Neuronal Arborization using Sparse Adeno-Associated Virus Labeling of Genetically Targeted Retinal Cell Populations

    Published on: March 19, 2021

    2.8K
    Sample Drift Correction Following 4D Confocal Time-lapse Imaging
    10:04

    Sample Drift Correction Following 4D Confocal Time-lapse Imaging

    Published on: April 12, 2014

    16.3K
  • 拟议的方法提供了对光子ToF分布的准确检索.

    • 结论:

    • 深度学习提供了一种灵活而准确的方法,用于在时间解决的FLI中进行解卷.
    • 开发的模型简化了这个过程,克服了传统方法的局限性.
    • 这种技术显示出在推进分散光学成像和FLI应用方面的巨大潜力.