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Abstract #4860

Velocity to Pressure Mapping in Stenotic Pulsatile Flows with an Encode-Decoder Deep Network

Ruponti Nath1, Amirkhosro Kazemi1, Marcus Stoddard2, and Amir Amini1
1ECE, University of Louisville, Louisville, KY, United States, 2Cardiovascular Division, Robley Rex VA Medical Centre, Louisville, KY, United States


We propose a novel deep learning based approach to estimate pressure drop inside a stenotic valve from 4D Flow MRI velocities. A neural network architecture learns the relationship of three directional velocities and predicts pressure as output. The network was tested on real 4D flow MRI data of aortic valvular flow both in-vitro and in vivo. Estimated in-vitro pressure drop by proposed method shows (3-5)% relative pressure drop error with corresponding CFD pressure. Estimated In-vivo pressure drop was also compared with doppler Echocardiography and simplified and modified Bernoulli at peak systole timepoints in 10 patients with aortic stenosis.

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