Zilverling

P.T. Rygiel MSc (Patryk)

Promovendus
EEMCS-AM-MIA
Zilverling 2110
p.t.rygiel@utwente.nl
+31534891422
Visitekaartje

Expertises

  • Engineering

    • Computational Fluid Dynamics
    • Deep Learning Method
    • Discretization
    • Surrogate Model

  • Computer Science

    • Geometric Deep Learning

  • Medicine and Dentistry

    • Coronary Artery
    • Hemodynamic
    • Steady State

Organisaties

Publicaties

2025

Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior (2025)Computer methods and programs in biomedicine, 271. Article 108958. Suk, J., Nannini, G., Rygiel, P., Brune, C., Pontone, G., Redaelli, A. & Wolterink, J. M.https://doi.org/10.1016/j.cmpb.2025.108958Learning hemodynamic scalar fields on coronary artery meshes: A benchmark of geometric deep learning models (2025)Computers in biology and medicine, 195. Article 110477. Nannini, G., Suk, J., Rygiel, P., Saitta, S., Mariani, L., Maranga, R., Baggiano, A., Pontone, G., Wolterink, J. M. & Redaelli, A.https://doi.org/10.1016/j.compbiomed.2025.110477Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models (2025)[Working paper › Preprint]. ArXiv.org. Rygiel, P., Suk, J., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2507.22817Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces (2025)[Working paper › Preprint]. ArXiv.org. Alblas, D., Rygiel, P., Suk, J., Kappe, K. O., Hofman, M., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2506.08729Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation (2025)[Working paper › Preprint]. ArXiv.org. Rygiel, P., Suk, J., Yeung, K. K., Brune, C. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2503.03453Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models (2025)[Working paper › Preprint]. ArXiv.org. Nannini, G., Suk, J., Rygiel, P., Saitta, S., Mariani, L., Maragna, R., Baggiano, A., Pontone, G., Wolterink, J. M. & Redaelli, A.https://doi.org/10.48550/arXiv.2501.09046

2024

Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior (2024)[Working paper › Preprint]. ArXiv.org. Suk, J., Nannini, G., Rygiel, P., Brune, C., Pontone, G., Redaelli, A. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2410.11920Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging (2024)[Working paper › Preprint]. ArXiv.org. Garzia, S., Rygiel, P., Dummer, S., Cademartiri, F., Celi, S. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2407.20728AAA-100: A Curated Dataset of 3D Watertight Abdominal Aortic Aneurysm Models (2024)[Dataset Types › Dataset]. Zenodo. Rygiel, P., Alblas, D., Brune, C., Smorenburg, S., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.5281/zenodo.10932956Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation (2024)[Working paper › Preprint]. ArXiv.org. Rygiel, P., Alblas, D., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.48550/arXiv.2403.15314

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