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Abstract #4859

AI-based Fully Automated 4D Flow Image Reconstruction and Post-Processing Pipeline in under 10 minutes.

Haben Berhane1, Michael Scott1, Ashitha Pathrose1, Patrick McCarthy2, Chris Malaisrie2, Bradley Allen3, Ryan Avery3, and Michael Markl1
1Biomedical Engineering, Northwestern University, Chicago, IL, United States, 2Cardiac Surgery, Northwestern University, Chicago, IL, United States, 3Radiology, Northwestern University, Evanston, IL, United States


4D flow MRI provides comprehensive assessment of cardiovascular hemodynamics. However, the current clinical usage of standard 4D flow MRI is hindered by long scan times and extensive, time-consuming post-processing such as eddy current corrections, noise masking, and 3D vessel segmentation. We seek to address this by developing a fully automated image reconstruction and post-processing pipeline for highly-accelerated aortic 4D flow MRI (R=5.7-10.2). The fully automated pipeline was shown to provide good-to-excellent agreement in quantitative and hemodynamic measures to conventional 4D flow MRI (GRAPPA, R=2) with manual post-processing. Additionally, the pipeline only requires <10 minutes compared to 20 minutes manually.

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