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

Downsampling01:20

Downsampling

868
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
868
Upsampling01:22

Upsampling

744
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
744

You might also read

Related Articles

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

Sort by
Same author

Sex classification from hand X-ray images in pediatric patients: How zero-shot Segment Anything Model (SAM) can improve medical image analysis.

Computers in biology and medicine·2025
Same author

Adaptive filter with Riemannian manifold constraint.

Scientific reports·2023
Same author

Effects of using mobile augmented reality for simple interest computation in a financial mathematics course.

PeerJ. Computer science·2021
Same author

A Weighted and Distributed Algorithm for Range-Based Multi-Hop Localization Using a Newton Method.

Sensors (Basel, Switzerland)·2021
Same author

Reconstruction of PET Images Using Cross-Entropy and Field of Experts.

Entropy (Basel, Switzerland)·2020
Same author

Driving Maximal Frequency Content and Natural Slopes Sharpening for Image Amplification with High Scale Factor.

Current medical imaging reviews·2020
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Apr 27, 2026

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
06:25

Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

Published on: January 19, 2024

1.6K

Noise reduction in small-animal PET images using a multiresolution transform.

Jose M Mejia, Humberto de Jesús Ochoa Domínguez, Osslan Osiris Vergara Villegas

    IEEE Transactions on Medical Imaging
    |June 22, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new multiresolution denoising method for small animal positron emission tomography (PET) images. The approach effectively reduces noise by 26% while preserving crucial image contrast and structures.

    More Related Videos

    High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
    11:09

    High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

    Published on: December 16, 2022

    3.7K
    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    3.8K

    Related Experiment Videos

    Last Updated: Apr 27, 2026

    Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States
    06:25

    Author Spotlight: Comparative Imaging of Neural Activity in Awake and Freely Moving States

    Published on: January 19, 2024

    1.6K
    High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals
    11:09

    High-Resolution Cardiac Positron Emission Tomography/Computed Tomography for Small Animals

    Published on: December 16, 2022

    3.7K
    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging
    07:15

    Transient Optical Clearing Using Absorbing Molecules for Ex Vivo and In Vivo Imaging

    Published on: July 11, 2025

    3.8K

    Area of Science:

    • Medical Imaging
    • Image Processing
    • Nuclear Medicine

    Background:

    • Positron Emission Tomography (PET) imaging is crucial for small animal research.
    • Reconstructed PET images often suffer from noise, hindering accurate analysis.
    • Existing denoising methods may compromise image contrast and structural integrity.

    Purpose of the Study:

    • To develop and validate a novel multiresolution denoising technique for small animal PET images.
    • To improve noise reduction efficacy while preserving image contrast and important structures.
    • To offer a flexible approach applicable with various image transforms.

    Main Methods:

    • A multiresolution analysis is applied in the transform domain.
    • PET image subbands are modeled as regions (homogeneous/heterogeneous) with estimated boundaries.
    • Independent filtering is performed using linear and surface polynomial estimators.
    • A modified edge focusing filter is used for boundary estimation.

    Main Results:

    • Achieved up to 26% reduction in the %STD of reconstructed small animal NEMA phantom images.
    • Demonstrated superior contrast preservation for simulated lesions compared to state-of-the-art methods.
    • Validated the method's effectiveness through experimental validation.

    Conclusions:

    • The proposed method significantly reduces noise in small animal PET images.
    • It effectively preserves image contrast and vital structures like lesions.
    • This technique offers a valuable tool for enhancing the quality of PET imaging analysis.