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

Oxidation-Reduction Reactions03:11

Oxidation-Reduction Reactions

75.5K
Oxidation–Reduction Reactions
75.5K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.8K
2.8K
Network Covalent Solids02:18

Network Covalent Solids

16.1K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.1K
Neural Regulation01:37

Neural Regulation

43.3K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
43.3K
Block Diagram Reduction01:22

Block Diagram Reduction

552
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
552

You might also read

Related Articles

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

Sort by
Same author

Enhancing Ultrasound Molecular Imaging: Toward Real-Time RPCA-Based Filtering to Differentiate Bound and Free Microbubbles.

IEEE transactions on ultrasonics·2026
Same author

Improved Nondestructive Ultrasound Molecular Imaging with Lightweight Convolutional Neural Network.

IEEE transactions on medical imaging·2026
Same author

The Contrast Order: An Order-Based Image Quality Criterion for Nonlinear Beamformers.

ArXiv·2026
Same author

UltraFlex: Iterative Model-Based Ultrasonic Flexible-Array Shape Calibration.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control·2025
Same author

Enhancing Ultrasound Molecular Imaging: Toward Real-Time RPCA-Based Filtering to Differentiate Bound and Free Microbubbles.

ArXiv·2025
Same author

Ultrasound Autofocusing: Common Midpoint Phase Error Optimization via Differentiable Beamforming.

IEEE transactions on medical imaging·2025

Related Experiment Video

Updated: Jan 27, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K

Beamforming and Speckle Reduction Using Neural Networks.

Dongwoon Hyun, Leandra L Brickson, Kevin T Looby

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |March 15, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Neural networks can reduce speckle noise in ultrasound B-mode images. This framework, trained on simulated data, effectively reduces speckle while preserving image resolution in real-world applications.

    More Related Videos

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM
    19:16

    Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM

    Published on: August 5, 2009

    16.5K

    Related Experiment Videos

    Last Updated: Jan 27, 2026

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    1.0K
    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.9K
    Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM
    19:16

    Live Cell Imaging of F-actin Dynamics via Fluorescent Speckle Microscopy FSM

    Published on: August 5, 2009

    16.5K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Signal Processing

    Background:

    • Ultrasound B-mode images suffer from speckle noise due to subresolution scatterer interference.
    • Traditional beamforming methods struggle to effectively mitigate this noise, impacting image quality.

    Purpose of the Study:

    • To develop and validate a neural network framework for speckle reduction in ultrasound B-mode imaging.
    • To introduce novel log-domain normalization-independent loss functions tailored for ultrasound data.

    Main Methods:

    • A fully convolutional neural network was trained using simulated ultrasound channel signals and ground-truth echogenicity maps.
    • Networks accepted 16 beamformed subaperture radio frequency (RF) signals and were optimized using a combination of l1 and multiscale structural similarity (MS-SSIM) losses.
    • Performance was evaluated on simulated, phantom, and in vivo data, comparing against delay-and-sum (DAS) and nonlocal means methods.

    Main Results:

    • The most effective configuration utilized a 16-layer, 32-filter network.
    • The neural network approach significantly outperformed DAS and spatial compounding in speckle reduction, maintaining resolution.
    • Improved detail preservation was observed compared to the nonlocal means method.

    Conclusions:

    • Machine-learned neural networks are a feasible approach for ultrasound B-mode image reconstruction.
    • Networks trained in silico can generalize to in vivo data, producing significantly speckle-reduced images.
    • This framework offers a promising advancement for enhancing ultrasound image quality.