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Related Experiment Videos

Exponential Entropy Driven HUM on Knee MR Images.

Edoardo Ardizzone1, Roberto Pirrone, Orazio Gambino

  • 1Universita' di Palermo - Dipartimento di Ingegneria Informatica - viale delle Scienze - edificio 6-C.A.P. 90128-PALERMO (ITALY)email: ardizzon@unipa.it.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
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Radiofrequency (RF) inhomogeneity in Magnetic Resonance Images causes illumination variations. This study introduces a novel information theory-based measure to determine the cut-off frequency for artifact correction.

Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Radiofrequency (RF) inhomogeneity is a significant artifact in Magnetic Resonance Imaging (MRI).
  • This artifact causes illumination variations, impacting clinical diagnosis and automated image segmentation.
  • Existing homomorphic filtering methods lack a defined approach for determining the critical cut-off frequency.

Purpose of the Study:

  • To introduce a novel measure for determining the cut-off frequency in RF inhomogeneity artifact correction.
  • To validate the proposed measure using a comprehensive experimental setup.

Main Methods:

  • Development of a new measure based on information theory principles.
  • Application of the measure to address RF inhomogeneity in MRI.

Related Experiment Videos

  • Extensive experimental validation of the proposed method.

    • Main Results:

    • The proposed information theory-based measure effectively determines the cut-off frequency for RF inhomogeneity.
    • Experimental results demonstrate the validity and efficacy of the developed approach.
    • The method aids in mitigating illumination variations caused by RF inhomogeneity.

    Conclusions:

    • The presented information theory-based measure offers a robust solution for RF inhomogeneity artifact correction in MRI.
    • This approach enhances image quality for both visual inspection and algorithmic analysis.
    • The study provides a crucial tool for improving the reliability of MRI data.