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 Experiment Videos

Correction of bias field in MR images using singularity function analysis.

Jianhua Luo1, Yuemin Zhu, Patrick Clarysse

  • 1Department of Biomedical Engineering, Shanghai Jiaotong University, Shanghai, China. jhluo@sjtu.edu.cn

IEEE Transactions on Medical Imaging
|August 12, 2005
PubMed
Summary

This study introduces a novel singularity function analysis (SFA) method to correct bias fields in magnetic resonance (MR) images. The approach effectively removes and reconstructs image components to improve MR image quality without prior assumptions.

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

De Novo Synthesis in Cancer Immunotherapy: Applications and Prospects.

MedComm·2026
Same author

Profilin-1 Deficiency Activates STING to Drive T Cell-Mediated Anti-Tumor Immunity in Breast Cancer.

bioRxiv : the preprint server for biology·2026
Same author

Clinical characteristics of <i>Pneumocystis jirovecii</i> pneumonia in non-HIV patients: a retrospective study.

Frontiers in cellular and infection microbiology·2026
Same author

A biodegradable low-voltage soft actuator with exceptional energy density and ultrafast response.

Science advances·2026
Same author

Primary glioblastoma mimicking brain metastasis in ALK-positive lung adenocarcinoma: a case report and literature review.

Frontiers in oncology·2026
Same author

Visualizing definitional divergence in high-dimensional data by manifold alignment: Application to 3D right ventricular strain computations.

IEEE transactions on medical imaging·2026

Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Bias fields are common artifacts in magnetic resonance (MR) imaging, distorting image intensity and affecting diagnostic accuracy.
  • Existing bias field correction methods often rely on assumptions about image content that may not always hold true.

Purpose of the Study:

  • To propose and evaluate a new bias field correction method for MR images using singularity function analysis (SFA).
  • To address limitations of current methods by not assuming higher spatial frequencies for anatomical information.

Main Methods:

  • Utilizing a mathematical model of singularity function analysis (SFA) to represent image signals.
  • Separating low spatial frequency components (corrupted by bias field) from high spatial frequency components (unpolluted).

Related Experiment Videos

  • Reconstructing the image from unpolluted components to estimate and correct the bias field.

    • Main Results:

    • The proposed SFA-based method successfully corrects bias fields in both simulated and real clinical MR images.
    • The approach demonstrates effectiveness without making prior assumptions about the spatial frequency distribution of anatomical information.

    Conclusions:

    • Singularity function analysis offers a robust and versatile approach for bias field correction in MR imaging.
    • This method provides a valuable tool for enhancing the quality and reliability of MR image analysis.