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Related Experiment Video

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Cardiac Magnetic Resonance Imaging at 7 Tesla
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Automatic basal slice detection for cardiac analysis.

Mahsa Paknezhad1, Stephanie Marchesseau2, Michael S Brown3

  • 1National University of Singapore , School of Computing, Department of Computer Science, Media Research Lab 4, AS6, Computing 1, 13 Computing Drive, 117417, Singapore.

Journal of Medical Imaging (Bellingham, Wash.)
|September 24, 2016
PubMed
Summary
This summary is machine-generated.

Accurately identifying the basal slice in cardiac imaging is crucial for measuring left ventricular ejection fraction. This study introduces an automated tool using the two-chamber view, achieving 92% accuracy in end-systole and 84% in end-diastole.

Keywords:
MRIactive shape modelbasal slice selectioncardiaclong-axis viewtwo-chamber view

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Area of Science:

  • Cardiovascular Imaging
  • Medical Image Analysis
  • Computational Anatomy

Background:

  • Accurate basal slice identification is essential for quantifying left ventricular ejection fraction in cardiac imaging.
  • Manual basal slice identification is subjective and prone to interobserver variability, necessitating automated solutions.
  • Current automated methods often misidentify the basal slice due to reliance on arbitrary anatomical landmarks.

Purpose of the Study:

  • To develop an automated algorithm for precise basal slice identification in cardiac magnetic resonance imaging.
  • To create a tool that adheres to Society for Cardiovascular Magnetic Resonance guidelines for basal slice determination.
  • To overcome limitations of existing methods in accurately identifying the basal slice, especially with suboptimal image quality.

Main Methods:

  • Utilized the two-chamber view in cardiac imaging to determine the basal slice.
  • Trained an active shape model to segment the two-chamber view and generate temporal binary profiles.
  • Developed a novel approach leveraging these profiles for automated basal slice identification according to guidelines.

Main Results:

  • The proposed automated tool achieved 92% accuracy in basal slice detection during end-systole.
  • The method demonstrated 84% accuracy in basal slice detection during end-diastole.
  • The algorithm successfully identified the basal slice by utilizing the two-chamber view, aligning with established guidelines.

Conclusions:

  • The developed automated tool provides an accurate and reliable method for basal slice identification in cardiac imaging.
  • This approach offers a significant improvement over manual identification and existing automated methods, reducing interobserver variability.
  • The tool's reliance on the two-chamber view and adherence to guidelines make it a valuable asset for consistent ejection fraction measurement.