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Abstract #0087

Stochastic Flow Co-expression Signatures: A novel concept for volumetric 4D flow assessment with application to aortic valve disease

Mohammed S.M. Elbaz1, Michael B. Scott1, Alex J. Barker2, Patrick McCarthy3, Chris Malaisrie3, Jeremy D. Collins4, Robert O. Bonow5, James Carr1, and Michael Markl1

1Radiology, Northwestern University, Chicago, IL, United States, 2University of Colorado, Anschutz Medical Campus, Aurora, Colo., CO, United States, 3Cardiac Surgery, Northwestern University, Chicago, IL, United States, 4Mayo Clinic, Rochester, MN, United States, 5Cardiology, Northwestern University, Chicago, IL, United States

Studies have shown an impact of aortic valve disease, as bicuspid aortic valve (BAV), on altered aortic blood flow. Nevertheless, aortic flow changes can be complex making objective visual assessment a challenging task. Existing quantitative flow metrics are useful, but each reflects only partial components of the overall complex flow changes. Here we propose a novel concept that uniquely captures the signature of normal and altered volumetric aortic flow changes derived from 4D Flow MRI. We demonstrated the high reproducibility and the feasibility of the derived flow signature in capturing distinctly altered flow signatures in the aorta of BAV patients.

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