Automated endocardial border detection from CMR images showed high correlation (r>0.96) and small biases (volumes: -6 mL; EF: 4.6%) compared to manual tracing for quantifying LV size and function.
Observational (n=36)
Does an automated endocardial border detection technique accurately quantify left ventricular size and function compared to manual tracing in patients undergoing cardiac magnetic resonance imaging?
Automated endocardial border detection from CMR images is fast and accurately quantifies LV size and function compared to manual tracing.
Effect estimate: r>0.96
PURPOSE: To develop a technique based on image noise distribution for automated endocardial border detection from cardiac magnetic resonance (CMR) images throughout the cardiac cycle, validate it, and test its clinical utility. MATERIALS AND METHODS: Images obtained in 36 patients were analyzed using custom software to obtain left ventricular (LV) volume throughout the cardiac cycle, end-systolic and end-diastolic LV volumes, and ejection fraction (EF). Validation against manually-traced endocardial boundaries included intertechnique comparisons of LV volumes, slice areas, and border positions. Then, the clinical feasibility of the dynamic automated analysis of LV function was tested in 14 patients with normal LV function, 12 patients with systolic dysfunction, and 10 patients with diastolic dysfunction. RESULTS: Analysis time for one cardiac cycle was 0.96), small biases (volumes: -6 mL; EF: 4.6%) and narrow limits of agreement (volumes: 17.6 mL; EF: 9.2%). We found significant intergroup differences in multiple quantitative indices of systolic and diastolic function. CONCLUSION: Fast, automated, dynamic detection of LV endocardial boundaries is feasible and allows accurate quantification of LV size and function, which is potentially clinically useful for objective assessment of systolic and diastolic dysfunction.
Corsi et al. (Wed,) conducted a observational in Left ventricular systolic and diastolic dysfunction (n=36). Automated endocardial border detection vs. Manually-traced endocardial boundaries was evaluated on Intertechnique comparisons of LV volumes and ejection fraction (r>0.96). Automated endocardial border detection from CMR images showed high correlation (r>0.96) and small biases (volumes: -6 mL; EF: 4.6%) compared to manual tracing for quantifying LV size and function.