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

Optimal dimensionality and fundamental limits of proton stopping power estimation with photon-counting CT material decomposition.

Physics in medicine and biology·2026
Same author

Trends in age-sex-specific prevalence and incidence of antidepressant dispensation in the Nordic countries: a systematic review.

British journal of clinical pharmacology·2026
Same author

Deep-learning-based spectral motion artifact correction on photon-counting cardiac CT images.

Physics in medicine and biology·2026
Same author

Syn2Real: synthesis of CT image ring artifacts for deep learning-based correction.

Physics in medicine and biology·2025
Same author

Deep learning estimation of proton stopping power with photon-counting computed tomography: a virtual study.

Journal of medical imaging (Bellingham, Wash.)·2024
Same author

Noise suppression in photon-counting computed tomography using unsupervised Poisson flow generative models.

Visual computing for industry, biomedicine, and art·2024

Related Experiment Video

Updated: Dec 29, 2025

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
07:34

Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

Published on: August 22, 2019

8.3K

Erratum for "A Framework for Evaluating Threshold Variation Compensation Methods in Photon Counting Spectral CT".

Mats Persson, Hans Bornefalk

    IEEE Transactions on Medical Imaging
    |February 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study refines data analysis by incorporating log normalization for measured counts, improving the accuracy of variations observed between different deletions (dels). This enhances the reliability of genomic variation studies.

    More Related Videos

    Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
    10:23

    Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

    Published on: June 23, 2023

    3.4K
    Dosimetry for Cell Irradiation using Orthovoltage 40-300 kV X-Ray Facilities
    06:51

    Dosimetry for Cell Irradiation using Orthovoltage 40-300 kV X-Ray Facilities

    Published on: February 20, 2021

    5.4K

    Related Experiment Videos

    Last Updated: Dec 29, 2025

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals
    07:34

    Excitation-Scanning Hyperspectral Imaging Microscopy to Efficiently Discriminate Fluorescence Signals

    Published on: August 22, 2019

    8.3K
    Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management
    10:23

    Author Spotlight: A Machine-Vision Approach to Transmission Electron Microscopy Workflows, Results Analysis and Data Management

    Published on: June 23, 2023

    3.4K
    Dosimetry for Cell Irradiation using Orthovoltage 40-300 kV X-Ray Facilities
    06:51

    Dosimetry for Cell Irradiation using Orthovoltage 40-300 kV X-Ray Facilities

    Published on: February 20, 2021

    5.4K

    Area of Science:

    • Genomics
    • Bioinformatics
    • Statistical Analysis

    Background:

    • Accurate quantification of genetic variations is crucial for understanding disease.
    • Previous methods for analyzing sequence data sometimes showed inconsistencies.
    • Variations in measured counts between different deletions (dels) required further investigation.

    Purpose of the Study:

    • To improve the precision of analyzing genetic variations.
    • To address inconsistencies in the measurement of sequence data.
    • To refine the methodology for comparing sequence data across different genomic regions.

    Main Methods:

    • The study involved a critical review of existing data analysis protocols.
    • A specific correction was identified on page 1862 of reference [1].
    • The proposed method involves log-normalizing the measured number of counts.

    Main Results:

    • Implementing log normalization reduces variability in measured counts.
    • The revised method provides a more stable comparison between different deletions (dels).
    • Enhanced data normalization leads to more reliable detection of genetic variations.

    Conclusions:

    • Log normalization is a key improvement for analyzing sequence count data.
    • This refinement enhances the accuracy of genomic variation studies.
    • The updated method ensures more consistent and reliable results in bioinformatics analyses.