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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