Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, segmentation; 4D flow; aorta
Motivation: Few studies have explored segmenting both the supra-aortic trunks and the thoracic aorta, which is essential for analyzing hemodynamics in 4D flow.
Goal(s): The automatic segmentation of the aorta and the supra-aortic trunks from 4D flow MRI.
Approach: We trained a model to segment the aorta in systolic and diastolic phases and the supra-aortic trunks in systolic phase from both magnitude and phase images.
Results: Segmentation of the entire aorta was consistent across systolic and diastolic phases. For supra-aortic trunks, the Dice coefficient was 0.72±0.07 and the Hausdorff distance was 5.42±2.53 mm, which are acceptable given the trunks' small size and complex shape.
Impact: We proposed an automatic approach to segment the aorta on different time steps from magnitude images from 4D flow MRI. Additionally, we presented the outcomes of segmenting the supra-aortic trunks using phase and magnitude from the systolic period.
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