6 citations · 10 across the 13 of their papers we have counts for
5 papers · 1 filter
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel +3
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed…
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
Patryk Rygiel, Julian Suk, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are pathologic dilatations of the abdominal aorta posing a high fatality risk upon rupture. Studying AAA progression and rupture risk often involv…
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Dieuwertje Alblas, Patryk Rygiel, Julian Suk +5
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Current clinical guidelines recomme…
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters ca…
Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging
Simone Garzia, Patryk Rygiel, Sven Dummer +3
Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. Howe…