Abstract
Myocardial iron loading thalassemia patients could be identified using T2* magnetic resonance images (MRI). To quantitatively assess cardiac iron loading, we proposed an effective algorithm to segment aligned free induction decay sequential myocardium images based on morphological operations and geodesic active contour (GAC). Nine patients with thalassemia major were recruited (10 male and 16 female) to undergo a thoracic MRI scan in the short axis view. Free induction decay images were registered for T2* mapping. The GAC were utilized to segment aligned MR images with a robust initialization. Segmented myocardium regions were divided into sectors for a region-based quantification of cardiac iron loading. Our proposed automatic segmentation approach achieve a true positive rate at 84.6% and false positive rate at 53.8%. The area difference between manual and automatic segmentation was 25.5% after 1000 iterations. Results from T2* analysis indicated that regions with intensity lower than 20 ms were suffered from heavy iron loading in thalassemia major patients. The proposed method benefited from abundant edge information of the free induction decay sequential MRI. Experiment results demonstrated that the proposed method is feasible in myocardium segmentation and was clinically applicable to measure myocardium iron loading.
Highlights
Epicardium[7,8]
The level set representation has been widely used in image processing and computer vision due to its implicit, intrinsic, parameter and topology freedom[14]
We proposed to complete the segmentation process based on the integrated edge information of different signal intensity peak images during T2* data acquisition and to use a geodesic active contour (GAC) method to improve the performance of the myocardium segmentation for iron loading assessment
Summary
Epicardium[7,8]. accurate segmentation of the myocardium in cardiac MRI is a labor intensive work for an experienced cardiologist, which limits its applications. To further improve the segmentation performance, the level set based active contour model has drawn the attention from a wide range of researchers from different fields. Lynch et al adopted a coupled level-set based active contour model to accurately perform left-ventricle myocardium segmentation[19]. The hybrid method with level set and active contour could be extended to higher dimensions This technique suffered from a long computational time cost. We proposed to complete the segmentation process based on the integrated edge information of different signal intensity peak images during T2* data acquisition and to use a geodesic active contour (GAC) method to improve the performance of the myocardium segmentation for iron loading assessment
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