Fully automatic left ventricular ejection fraction evaluation in magnetic resonance images using patch-based clustering technique
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Abstract
Background: Left Ventricular Ejection Fraction (LVEF) has been used in evaluating cardiac function. It is calculated generally by the difference between end-diastolic volumes (EDV) and end-systolic volumes (ESV). In order to obtain the EDV and ESV from magnetic resonance imaging (MRI), an experienced cardiologist is required to select the smallest and largest axial images from 30 phases in each slice that represent the ESV and EDV, respectively. This process is time consuming and could have individual-dependent variability.
Objectives: To develop an algorithm that determines the EDV and ESV, and automatically calculates the LVEF as an output in MRI in order to reduce operation time and interobserver variability for the user.
Materials and methods: Fifteen thalassemia patients were recruited into this study. Image data were acquired using a 1.5 Tesla MRI scanner. Scanning protocol included field of view (FOV) of 300 mm, matrix size of 256×256, slice thickness of 8 mm, pixel spacing of 1.25 mm, and repetition time (TR) and echo time (TE) of 3.83 and 1.69 ms, respectively. The proposed algorithm included 3 steps; left ventricle (LV) segmentation, end diastolic (ED) and end systolic (ES) phase selection, and LVEF calculation. The LV region was segmented by using the patch-based clustering method. Shape of the LV was tuned by mathematical morphologies and the Gaussian filter. The processes were repeated for all phases. The ED and ES phases were selected from those that had the maximum and minimum number of pixels, respectively, in the LV area. The EDV and ESV were the sum of the number of pixels in the segmented LV areas of the ED and ES phases, which were multiplied by pixel spacing and slice thickness from all slices. Finally, the LVEF was calculated and reported.
Results: Precision and recall were used to evaluate segmentation performance, which was good in the experimental results of the proposed method. Precision and recall were on average approximately 0.9 and 0.7 for the ED and ES phase, respectively. The different percentage values of the LVEF was 3.32% between the proposed and manual segmentation method.
Conclusion: This work proposed automatic LVEF evaluation from MRI. Patch-based clustering techniques, mathematical morphologies and the Gaussian filter were used to segment the LV area automatically. Experimental results showed that the proposed method could evaluate LVEF values that were close to those from two experts who used the manual segmentation method.
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