Zilverling

P.T. Rygiel MSc (Patryk)

PhD Candidate
EEMCS-AM-MIA
Zilverling 2110
p.t.rygiel@utwente.nl
+31534891422
Business card

Expertise

  • Computer Science

    • Geometric Deep Learning

  • Engineering

    • Computational Fluid Dynamics
    • Deep Learning Method
    • Boundary Condition
    • Surrogate Model

  • Medicine and Dentistry

    • Coronary Artery
    • Hemodynamic
    • Steady State

Organisations

Publications

2025

Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces (2025)Artificial intelligence in medicine, 172. Article 103323 (E-pub ahead of print/First online). Alblas, D., Rygiel, P., Suk, J., Kappe, K. O., Hofman, M., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.1016/j.artmed.2025.103323Deep 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.2501.09046Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation (2025)In Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers: 15th International Workshop, STACOM 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Revised Selected Papers (pp. 269-279) (Lecture Notes in Computer Science; Vol. 15448). Springer. Rygiel, P., Alblas, D., Brune, C., Yeung, K. K. & Wolterink, J. M.https://doi.org/10.1007/978-3-031-87756-8_27

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.2407.20728

Other contributions

  • Rygiel, P., Płuszka, P., Zieba, M., Konopczyński, T. (2023). CenterlinePointNet++: A New Point Cloud Based Architecture for Coronary Artery Pressure Drop and vFFR Estimation. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_73
  • Rygiel Patryk, Zieba Maciej and Konopczynski Tomasz. “Eigenvector Grouping for Point Cloud Vessel Labeling.” Proceedings of the First International Workshop on Geometric Deep Learning in Medical Image Analysis, edited by Erik Bekkers et al., vol. 194, PMLR, 2022, pp. 72–84, https://proceedings.mlr.press/v194/rygiel22a.html.
  • Gajowczyk Milosz*, Rygiel Patryk*, Grodek Piotr, Korbecki Adrian, Sobanski Michal, Podgorski Przemyslaw and Konopczynski Tomasz. “Coronary Ostia Localization Using Residual U-Net with Heatmap Matching and 3D DSNT.” Machine Learning in Medical Imaging (MLMI 2022) held with MICCAI 2022, edited by Chunfeng Lian et al., Springer Nature Switzerland, 2022, pp. 318–27. (* equal contribution) 

Research profiles

Address

University of Twente

Zilverling (building no. 11), room 2110
Hallenweg 19
7522 NH Enschede
Netherlands

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