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

162
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...
162

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

Polarization-Sensitive Module for Optical Coherence Tomography Instruments.

IEEE transactions on medical imaging·2026
Same author

HoloBio: A holographic microscopy tool for quantitative biological analysis.

PLoS computational biology·2026
Same author

Efficient algorithm for optimal wavelength selection in photoacoustic spectral unmixing.

Biomedical optics express·2026
Same author

Polarization-sensitive optical coherence tomography-based fully-automated volumetric coronary fibrous cap characterization.

European heart journal. Imaging methods and practice·2026
Same author

Erratum: Wavenumber-space wavefront sensorless adaptive-optics for optical coherence tomography: publisher's note.

Biomedical optics express·2026
Same author

FUSION: A fast and uniform processing framework for whole-brain optical scattering tomography images.

bioRxiv : the preprint server for biology·2026

相关实验视频

Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K

在OCT使用深度学习进行概率学体积光斑抑制.

Bhaskara Rao Chintada1,2, Sebastián Ruiz-Lopera1,3, René Restrepo4

  • 1Wellman Center for Photomedicine, Massachusetts General Hospital, Boston, MA 02114, USA.

ArXiv
|December 18, 2023
PubMed
概括

我们开发了一种快速的深度学习方法,用于减少光学连贯性断层扫描 (OCT) 图像中的斑点. 这种AI框架有效地消除噪音,同时保留图像细节,改善各种医疗应用的可视化.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能

更多相关视频

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.7K
Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
00:09

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

6.9K

相关实验视频

Last Updated: Jul 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography
08:50

Longitudinal Morphological and Physiological Monitoring of Three-dimensional Tumor Spheroids Using Optical Coherence Tomography

Published on: February 9, 2019

7.7K
Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
00:09

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

6.9K
  • 生物医学工程 生物医学工程

    • 背景情况:

    • 光学连贯断层扫描 (OCT) 中的斑点噪声降低了图像质量,并阻碍了准确的诊断.
    • 开发有效的斑点减少技术对于增强OCT临床实用性至关重要.
    • 现有的方法往往难以保存细节,或是计算密集型.

    结论:

    • 开发的深度学习框架提供了一个快速有效的解决方案,以减少OCT的体积斑点.
    • 这种方法通过消除斑点噪声,同时保持结构完整性,显著提高图像质量.
    • 拟议的技术的可访问性和速度使其成为推进海外国家和地区成像应用的宝贵工具.