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Abstract #2089

Playing with FIRE: a framework for on-scanner, in-line fully automated 4D-Flow MRI reconstruction, pre-processing and flow visualization

Justin Baraboo1, Michael Scott1, Haben Berhane1, Ashitha Pathrose1, Michael Markl1, Ning Jin2, and Kelvin Chow1,2
1Northwestern, Chicago, IL, United States, 2Cardiovascular MR R&D, Siemens, Chicago, IL, United States

4D-Flow MRI is a valuable technique for quantifying cardiovascular hemodynamics in the aorta; however, it suffers from manual off-line post processing. To address this, we integrated our custom deep learning tools for automatic 4D-Flow processing within the on-scanner reconstruction environment through Siemen’s Framework for Image Reconstruction (FIRE) interface. We retrospectively reconstructed raw data from 10 patients with aortic dilation, valve repair and/or aneurysm as well as one, prospectively recruited, control on scanner. Our deep learning tools ran successfully, and an aortic velocity maximum intensity projection cine was generated and sent to the scanner’s console alongside the reconstructed 4D-flow.

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