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Automatic detection of left ventricular contours from cardiac cine magnetic resonance imaging using fuzzy logic
A Lalande1, L Legrand, P M Walker
1Laboratoire de Biophysique, Faculté de Médecine, Université de Bourgogne, Dijon, France.
Insights
This study introduces an automatic method for detecting left ventricle contours in cardiac MRI, significantly reducing time and subjectivity. The automated approach accurately calculates ejection fraction, a key indicator of heart function.
Area of Science:
- Cardiovascular Imaging
- Medical Physics
- Biomedical Engineering
Background:
- Gated cardiac cine MRI offers valuable dynamic data on left ventricular function.
- Manual extraction of parameters like myocardium wall thickness and left ventricular volumes is time-consuming and subjective.
- Observer contour tracing introduces variability and time constraints in cardiac MRI analysis.
Purpose of the Study:
- To develop and evaluate an automatic left ventricle contour-detection method.
- To reduce variability and time constraints associated with manual contour tracing in cardiac MRI.
- To assess the feasibility of using automated contour detection for physiological parameter extraction.
Main Methods:
- Application of fuzzy logic-based automatic contour detection.
- Identification of endocardial and epicardial borders in short-axis cardiac magnetic resonance images.
- Comparison of automatic contouring with manual tracing using ejection fraction as the criterion.
Main Results:
- A high correlation (r2 = 0.98) was observed between the automatic and manual contour detection methods.
- The automatic method demonstrated strong agreement with manual measurements for ejection fraction calculation.
- The developed method effectively identified left ventricular borders.
Conclusions:
- The automatic contour detection method accurately determines ejection fraction.
- This automated approach offers a reliable alternative to manual contour tracing for left ventricular function assessment.
- Fuzzy logic-based contour detection can streamline cardiac MRI analysis.
Rationale And Objectives:
Gated cardiac cine magnetic resonance imaging provides accurate dynamic data of the left ventricular function. However, the manual extraction of important physiologic parameters such as myocardium wall thickness and left ventricular volumes is invariably time consuming and subjective. To reduce the variability and time constraints inherent in observer contour tracing, the authors developed an automatic left ventricle contour-detection method.
Methods:
The purpose was to apply fuzzy logic-based automatic contour detection to identification of endocardial and epicardial borders in short-axis magnetic resonance images. The automatic contouring was compared with manual tracing using the calculated ejection fraction as the comparison criterion.
Results:
A good correlation was found between the two approaches (r2 = 0.98).
Conclusions:
The ejection fraction can be obtained using this automatic contouring method.