Improved automated quantification of left ventricular size and function from cardiac magnetic resonance images

Assessment of left ventricular (LV) size and function from cardiac magnetic resonance (CMR) images requires manual tracing of LV borders on multiple 2D slices, which is subjective, experience dependent, tedious and time-consuming. We tested a new method for automated dynamic segmentation of CMR images based on a modified region-based model, in which a level set function minimizes a functional containing information regarding the probability density distribution of the gray levels. Images (GE 1.5T FIESTA) obtained in 9 patients were analyzed to automatically detect LV endocardial boundaries and calculate LV volumes and ejection fraction (EF). These measurements were validated against manual tracing. The automated calculation ofLV volumes and EF was completed in each patient in <3 min and resulted in high level of agreement with no significant bias and narrow limits of agreement with the reference technique. The proposed technique allows fast automated detection of endocardial boundaries as a basis for accurate quantification of LV size and function from CMR images.