Search Results for author: Michael Markl

Found 3 papers, 1 papers with code

A deep learning approach to using wearable seismocardiography (SCG) for diagnosing aortic valve stenosis and predicting aortic hemodynamics obtained by 4D flow MRI

no code implementations5 Jan 2023 Mahmoud E. Khani, Ethan M. I. Johnson, Aparna Sodhi, Joshua Robinson, Cynthia K. Rigsby, Bradly D. Allen, Michael Markl

We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV).

MRI-MECH: Mechanics-informed MRI to estimate esophageal health

no code implementations15 Sep 2022 Sourav Halder, Ethan M. Johnson, Jun Yamasaki, Peter J. Kahrilas, Michael Markl, John E. Pandolfino, Neelesh A. Patankar

Dynamic magnetic resonance imaging (MRI) is a popular medical imaging technique to generate image sequences of the flow of a contrast material inside tissues and organs.

Machine-Learned Prediction Equilibrium for Dynamic Traffic Assignment

1 code implementation14 Sep 2021 Lukas Graf, Tobias Harks, Kostas Kollias, Michael Markl

We study a dynamic traffic assignment model, where agents base their instantaneous routing decisions on real-time delay predictions.

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