Abstract #3650

Deep learning-based prediction of aortic hemodynamics obtained by 4D flow MRI using seismocardiography of chest vibrations

Mahmoud Ebrahimkhani¹, Ethan Johnson¹, Aparna Sodhi², Joshua Robinson^2,3, Cynthia Rigsby², Bradly Allen³, and Michael Markl³

¹Radiology, Northwestern University, Chicago, IL, United States, ²Ann & Robert H. Lurie Children’s Hospital, Chicago, IL, United States, ³Northwestern University, Chicago, IL, United States

Synopsis

Keywords: Flow, HeartWe pursued a deep learning approach to investigate the utilization of a wearable seismocardiography (SCG) device to predict measures of flow similar to those obtained using 4D flow MRI. SCG can measure the chest vibrations caused by cardiac mechanical activities such as valve closures and changes of pulsatile flow. We hypothesized that deep learning can be used to infer the pathological changes in blood flow, such as a higher systolic peak velocity (Vmax) in patients with aortic valve diseases, from the SCG signals.

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