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

Eigenimage filtering in MR imaging.

J P Windham1, M A Abd-Allah, D A Reimann

  • 1Department of Diagnostic Radiology and Medical Imaging, Henry Ford Hospital, Detroit, MI 48202.

Journal of Computer Assisted Tomography
|January 1, 1988
PubMed
Summary
This summary is machine-generated.

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

Assessment of the hazard risks on HVDC transmission networks due to lightning strikes and faults.

PloS one·2024
Same author

ROC study and SUV threshold using quantitative multi-modal SPECT for bone imaging.

European journal of hybrid imaging·2021
Same author

Magnetic Resonance Imaging for Meniscal Tears of the Knee.

The Physician and sportsmedicine·2016
Same author

Magnetic resonance imaging features of hip disorders in an Egyptian pediatric population.

Reumatismo·2015
Same author

Automated three-dimensional signature model for assessing brain injury in emergent stroke.

Cerebrovascular diseases (Basel, Switzerland)·2001
Same author

Unsupervised segmentation of multiparameter MRI in experimental cerebral ischemia with comparison to T2, diffusion, and ADC MRI parameters and histopathological validation.

Journal of magnetic resonance imaging : JMRI·2000
Same journal

Low-Field Neuroimaging: Opportunities and Limitations.

Journal of computer assisted tomography·2026
Same journal

Diagnostic Performance of Routine Abdominal MRI for Detecting Left Ventricular Hypertrophy in ADPKD.

Journal of computer assisted tomography·2026
Same journal

Evaluation of Gd-EOB-DTPA MRI With Diffusion and Clinicopathologic Features for Predicting Microvascular Invasion in Hepatocellular Carcinoma.

Journal of computer assisted tomography·2026
Same journal

Artificial Intelligence for Opportunistic Screening for Osteoporosis and Spine Fractures Using Computed Tomography: A Systematic Review and Meta-Analysis.

Journal of computer assisted tomography·2026
Same journal

Accuracy and Variability of Spatial Localization of Infarct Core Predicted by CT Perfusion.

Journal of computer assisted tomography·2026
Same journal

Acute Biliary Disorders and Complications.

Journal of computer assisted tomography·2026
See all related articles

Eigenimage filtering enhances specific features in magnetic resonance (MR) images by suppressing interfering signals. This technique optimizes linear filter weights using an eigenvector approach for improved image analysis.

Area of Science:

  • Medical Imaging
  • Image Processing
  • Biophysics

Background:

  • Magnetic Resonance (MR) imaging generates complex data requiring advanced processing.
  • Distinguishing specific features from interfering signals is crucial for accurate diagnosis.

Purpose of the Study:

  • To present the technical details of eigenimage filtering for MR imaging.
  • To demonstrate its application in enhancing desired features while suppressing unwanted ones.

Main Methods:

  • Eigenimage filtering utilizes a linear filter approach.
  • Weighting components are determined by maximizing a ratio criterion based on Rayleigh's principle.
  • The optimal weights correspond to the eigenvector of a generalized eigenvalue problem.

Related Experiment Videos

Main Results:

  • The method successfully enhances a desired feature in simulated and real MR image sequences.
  • Interfering features are effectively suppressed, leading to clearer image representation.
  • Application to normal and abnormal brain MR images demonstrates practical utility.

Conclusions:

  • Eigenimage filtering provides a robust method for feature enhancement in MR imaging.
  • The technique offers improved image interpretability for diagnostic purposes.
  • This approach holds significant potential for various MR imaging applications.