Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Calculation of state-to-state differential and integral cross sections for atom-diatom reactions with transition-state wave packets.

The Journal of chemical physics·2014
Same author

[Natural attenuation of tetracycline in the water of Taihu Lake under different environmental conditions].

Huan jing ke xue= Huanjing kexue·2014
Same author

The role of AhR in autoimmune regulation and its potential as a therapeutic target against CD4 T cell mediated inflammatory disorder.

International journal of molecular sciences·2014
Same author

Association between polymorphisms in the flanking region of the TAFI gene and atherosclerotic cerebral infarction in a Chinese population.

Lipids in health and disease·2014
Same author

An Updated Analysis with 85,939 Samples Confirms the Association Between CR1 rs6656401 Polymorphism and Alzheimer's Disease.

Molecular neurobiology·2014
Same author

Immobilized lipase from Candida sp. 99-125 on hydrophobic silicate: characterization and applications.

Applied biochemistry and biotechnology·2014

Related Experiment Video

Updated: Jul 6, 2025

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
09:43

Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

Published on: March 20, 2017

9.9K

Calibration-free quantitative phase imaging in multi-core fiber endoscopes using end-to-end deep learning.

Jiawei Sun, Bin Zhao, Dong Wang

    Optics Letters
    |January 9, 2024
    PubMed
    Summary

    A new learning-based quantitative phase imaging (QPI) method enables real-time endoscopic imaging using multi-core fibers (MCFs). This breakthrough significantly speeds up phase reconstruction, allowing for video-rate imaging in challenging environments.

    More Related Videos

    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
    05:52

    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

    Published on: October 20, 2019

    36.2K
    High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
    13:49

    High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

    Published on: January 11, 2011

    34.5K

    Related Experiment Videos

    Last Updated: Jul 6, 2025

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping
    09:43

    Transmission of Multiple Signals through an Optical Fiber Using Wavefront Shaping

    Published on: March 20, 2017

    9.9K
    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals
    05:52

    Multi-Fiber Photometry to Record Neural Activity in Freely-Moving Animals

    Published on: October 20, 2019

    36.2K
    High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging
    13:49

    High-resolution Fiber-optic Microendoscopy for in situ Cellular Imaging

    Published on: January 11, 2011

    34.5K

    Area of Science:

    • Biomedical Optics
    • Medical Imaging
    • Computational Imaging

    Background:

    • Quantitative phase imaging (QPI) offers label-free, in vivo endoscopic imaging.
    • Conventional QPI methods face computational limitations for real-time applications.
    • Multi-core fibers (MCFs) present a minimally invasive imaging pathway.

    Purpose of the Study:

    • To develop a learning-based QPI method for MCFs to overcome computational bottlenecks.
    • To achieve video-rate imaging speeds for enhanced endoscopic visualization.
    • To create an open-source dataset for training and validating MCF phase imaging algorithms.

    Main Methods:

    • Implemented a deep neural network (DNN) for rapid phase reconstruction.
    • Developed an optical system for automated dataset generation.
    • Utilized a dataset of 50,176 paired speckles and phase images for training.

    Main Results:

    • Reduced phase reconstruction time to 5.5 ms, enabling 181 fps imaging.
    • Achieved a mean phase reconstruction fidelity of up to 99.8%.
    • Successfully demonstrated robust performance in experimental settings.

    Conclusions:

    • The learning-based MCF phase imaging method significantly accelerates QPI.
    • This approach enables real-time, label-free endoscopic imaging in hard-to-reach areas.
    • The open-source dataset facilitates further research and development in fiber-based QPI.