Vortices and helices are crucial features of hemodynamic flow. Such structures may define new clinically relevant biomarkers when assessing cardiovascular pathologies mediated by abnormal flow patterns (e.g. aneurysm formation). Thus, retrieving such structures in time-resolved and velocity-encoded 3D PC-MRI image data is of tremendous interest. However, prior studies only focused on a voxel-wise identification, and are lacking meaningful quantitative metrics which characterize the full vortical flow pattern. The objective of this work is to propose metrics for fully automated detection and quantitative characterization of vortical flow patterns in the aorta.
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