Keywords: Data Processing, Software Tools, Exercise, real-time
This study aimed to enable ECG-free construction of respiratory controlled 3D cardiac magnetic resonance cine series from short-axis 2D real-time images through automated detection of cardiac phases. This is important for imaging during exercise, since obtaining a reliable ECG-signal can be difficult. Visually coherent cines could be constructed for midventricular slice positions through detecting cardiac phases from automated left ventricular segmentations in real-time timeframes, but not for apical and basal slice positions. Deep learning-based regression of cardiac phases directly from images was investigated to handle this problem, but apical and basal cine construction remains an open problem.
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