PURPOSE: To assess the use of a nonrigid registration technique for semi-automatic segmentation of the aorta from real-time velocity mapping MRI. MATERIALS AND METHODS: Real-time phase contrast images were acquired to measure flow and stroke volumes in 10 subjects, during free breathing, at rest, and during exercise. A nonrigid registration algorithm was developed to propagate a manually drawn region of interest in the aorta from one frame to all other frames of the real-time sequence (148 images). Thus the technique provided a semi-automatic segmentation over the whole sequence of images. The accuracy was assessed by comparison with manual segmentations in terms of Dice overlap measures and stroke volumes (SV). RESULTS: Semi-automatic segmentations were comparable to manual ones (Dice score of 0.89 ± 0.04). Inter-observer reproducibility was similar for manual and semi-automatic segmentations (Dice score of 0.90 ± 0.04 in both cases, the difference was not significant). SV measurements also showed good agreement between manual and semi-automatic segmentations (correlation coefficient r > 0.94), and the differences were not statistically significant. CONCLUSION: Although real-time phase contrast images have compromised image quality, a fast and robust segmentation of the aorta was possible using the registration-based technique.
PURPOSE: To assess the use of a nonrigid registration technique for semi-automatic segmentation of the aorta from real-time velocity mapping MRI. MATERIALS AND METHODS: Real-time phase contrast images were acquired to measure flow and stroke volumes in 10 subjects, during free breathing, at rest, and during exercise. A nonrigid registration algorithm was developed to propagate a manually drawn region of interest in the aorta from one frame to all other frames of the real-time sequence (148 images). Thus the technique provided a semi-automatic segmentation over the whole sequence of images. The accuracy was assessed by comparison with manual segmentations in terms of Dice overlap measures and stroke volumes (SV). RESULTS:Semi-automatic segmentations were comparable to manual ones (Dice score of 0.89 ± 0.04). Inter-observer reproducibility was similar for manual and semi-automatic segmentations (Dice score of 0.90 ± 0.04 in both cases, the difference was not significant). SV measurements also showed good agreement between manual and semi-automatic segmentations (correlation coefficient r > 0.94), and the differences were not statistically significant. CONCLUSION: Although real-time phase contrast images have compromised image quality, a fast and robust segmentation of the aorta was possible using the registration-based technique.
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