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Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...

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Related Experiment Video

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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking
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Published on: February 12, 2011

Myocardial borders segmentation from cine MR images using bidirectional coupled parametric deformable models.

Hisham Sliman1, Fahmi Khalifa, Ahmed Elnakib

  • 1BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292, USA.

Medical Physics
|September 7, 2013
PubMed
Summary

This study introduces a novel 3D deformable model for segmenting left ventricle (LV) walls in cardiac MRI. The method accurately estimates global cardiac performance indexes, outperforming existing techniques.

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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging
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Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

Area of Science:

  • Medical Imaging
  • Computational Biology
  • Cardiovascular Imaging

Background:

  • Accurate segmentation of the left ventricle (LV) is crucial for assessing cardiac function.
  • Existing cardiac MRI segmentation methods face challenges in speed and robustness.

Purpose of the Study:

  • To develop and evaluate a novel 3D (2D + time) deformable model for segmenting LV wall borders.
  • To assess the impact of the proposed segmentation method on the estimation of global cardiac performance indexes.

Main Methods:

  • Utilized first-order (adaptive linear combination of discrete Gaussians - LCDG) and second-order (Markov-Gibbs random field - MGRF) visual appearance features.
  • Employed a modified EM algorithm for LCDG parameter estimation and analytical computation for MGRF potentials.
  • Developed fast, robust, bidirectional coupled parametric deformable models for 3D segmentation.

Main Results:

  • Achieved an average Dice Similarity Coefficient (DSC) of 0.926 ± 0.022 and Average Distance (AD) of 2.16 ± 0.60 mm on cine CMR data.
  • Demonstrated superior performance compared to two other level set methods (average DSC: 0.904 ± 0.033 and 0.885 ± 0.02).
  • High segmentation accuracy translated to precise estimation of global cardiac performance indexes (ESV, EDV, EF).

Conclusions:

  • The proposed 3D deformable model offers superior performance for LV segmentation in cardiac MRI.
  • The method provides accurate estimation of global cardiac performance indexes, validated by Bland-Altman analyses.
  • Results on the MICCAI 2009 database confirm the approach's effectiveness over published methods.