Keywords: Flow, Data ProcessingAnalysis of pulse wave velocity by 4D flow MRI usually involves a manual processing pipeline with multiple steps of active use. We set out to automate the entire pipeline from original DICOMs to parameter quantification for evaluation of “whole-chest” 4D flow MRI. A deep learning algorithm trained on aortic acquisitions was used to facilitate segmentation. The supervised automation pipeline allowed for shorter analysis times and less user interaction, enabling the user to focus on refining aortic segmentations generated by deep learning. Results were comparable, but additional work including adjustment of preprocessing settings and retraining of CNN is warranted for optimization.
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