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

Bio-SCOPE: fast biexponential T<sub>1ρ</sub> mapping of the brain using signal-compensated low-rank plus sparse matrix decomposition.

Magnetic resonance in medicine·2019
Same author

Enantioselective Radical Ring-Opening Cyanation of Oxime Esters by Dual Photoredox and Copper Catalysis.

Organic letters·2019
Same author

ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING.

Proceedings. IEEE International Symposium on Biomedical Imaging·2019
Same author

Technical note: Development and application of KASP assays for rapid screening of 8 genetic defects in Holstein cattle.

Journal of dairy science·2019
Same author

Sesquiterpenes and diterpenes from Euphorbia thymifolia.

Fitoterapia·2019
Same author

Glechomanamides A-C, Germacrane Sesquiterpenoids with an Unusual Δ<sup>8</sup>-7,12-Lactam Moiety from <i>Salvia scapiformis</i> and Their Antiangiogenic Activity.

Journal of natural products·2019

Related Experiment Video

Updated: May 7, 2026

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
10:14

3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

Published on: May 12, 2019

6.7K

Noise behavior of MR brain reconstructions using compressed sensing.

Yuqiong Ding, Leslie Ying, Na Zhang

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
    PubMed
    Summary

    Compressed sensing (CS) MRI image reconstruction noise is not well understood. This study analyzes CS-MRI noise behavior, finding it non-uniform and increasing with undersampling, but predictable.

    More Related Videos

    Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
    08:33

    Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

    Published on: January 5, 2024

    2.0K
    Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
    15:18

    Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

    Published on: July 30, 2009

    17.2K

    Related Experiment Videos

    Last Updated: May 7, 2026

    3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol
    10:14

    3D Scanning Technology Bridging Microcircuits and Macroscale Brain Images in 3D Novel Embedding Overlapping Protocol

    Published on: May 12, 2019

    6.7K
    Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research
    08:33

    Author Spotlight: Methodologies and Advancements of Chronic Pain Management Research

    Published on: January 5, 2024

    2.0K
    Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
    15:18

    Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

    Published on: July 30, 2009

    17.2K

    Area of Science:

    • Medical Imaging
    • Signal Processing
    • Magnetic Resonance Imaging (MRI)

    Background:

    • Compressed sensing (CS) enables high-quality MRI reconstruction from undersampled data.
    • Clinical application of CS-MRI is hindered by unexplored noise characteristics.
    • Understanding noise behavior is crucial for reliable CS-MRI deployment.

    Purpose of the Study:

    • To analyze the noise behavior in compressed sensing MRI (CS-MRI) reconstructions.
    • To investigate the impact of varying reduction factors on noise characteristics.
    • To develop a model for predicting noise behavior in CS-MRI.

    Main Methods:

    • Brain CS-MRI reconstructions were performed using non-linear conjugate gradient (NLCG) solvers.
    • Noise behavior was characterized using the MP-Law method.
    • A fitting model was developed to predict noise parameters based on reduction factors.

    Main Results:

    • Spatial noise distribution in CS-MRI reconstructions was found to be non-uniform.
    • Noise variance increased with higher reduction factors (more undersampling).
    • A predictive model for noise behavior and noise amplification factor maps were generated.

    Conclusions:

    • CS-MRI noise is spatially non-uniform and dependent on the reduction factor.
    • The developed model aids in predicting and understanding noise in CS-MRI.
    • Results offer a quantitative understanding of noise in CS-MRI, supporting its clinical translation.