32 citations · 119 across the 19 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…
Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models
Guido Nannini, Julian Suk, Patryk Rygiel +7
Coronary artery disease, caused by the narrowing of coronary vessels due to atherosclerosis, is the leading cause of death worldwide. The diagnostic gold standard, fractional flow…