Measurement of mechanical properties of synthetic vessels using a Man-made Circulatory Model

Authors

  • Andrzej Polanczyk Faculty of Safety Engineering and Civil Protection, Fire University, 01-629 Warsaw, Poland
  • Aleksandra Piechota-Polanczyk Medical University of Lodz, Department of Cell Cultures and Genomic Analysis, 90-752 Lodz, Poland
  • Agnieszka W. Piastowska-Ciesielska Medical University of Lodz, Department of Cell Cultures and Genomic Analysis, 90-752 Lodz, Poland
  • Ihor Huk Department of Surgery, Division of Vascular Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
  • Christoph Neumayer Department of Surgery, Division of Vascular Surgery, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
  • Patricia Pia Wadowski Division of Angiology, Department of Internal Medicine II, Medical University of Vienna, Währinger Gürtel 18-20, 1090 Vienna, Austria
  • Julia Balcer Institute of Electronics, Lodz University of Technology, 93-590 Lodz, Poland
  • Michal Strzelecki Institute of Electronics, Lodz University of Technology, 93-590 Lodz, Poland

DOI:

https://doi.org/10.24425/mms.2025.155805

Keywords

iliac arteries, ring-enforced ePTFE vascular prostheses, Dacron vascular prostheses, artificial system

Abstract

Ex-vivo vessel culture systems that simulate hemodynamic factors enable the measurement of flow and pressure conditions. However, limited efforts have been made to use ex-vivo vessel bioreactors to accurately replicate the hemodynamic factors influencing arterial wall behaviour in an artificial setting. Therefore, the objective of this study was to examine mechanical properties of different types of vessels (vascular grafts and human arteries) under various hemodynamic conditions using a specialised ex vivo. In addition, the study compared different vascular grafts with human arteries. To achieve this, a Man-made Circulatory Model was designed and constructed to simulate vessel structure under varying flow conditions. The following vessel types were analysed: ring-enforced ePTFE prostheses, Dacron prostheses, and iliac arteries. The initial conditions for the process were: pulsating flow at five frequencies (60, 75, 90, 105, and 120 l/min) and varying ejection volumes (EV) of homemade fluid per cycle (70, 85, 100, and 115 ml). Our findings suggest that pulsation frequency and ejection volume are key factors that influence the performance of these vascular prostheses. In addition, we found that as pulsation frequency increases, the diameter dilation differences between the iliac arteries and prostheses, as well as between the two types of prostheses, also change (𝑝 < 0.0001). Specifically, the difference in dilation between the prostheses and the iliac arteries increases at higher frequencies, but the pattern of this increase varies between the prosthesis types, suggesting differences in their mechanical behaviour under varying conditions of flow (𝑝 < 0.0001). To sum up, our approach may help guide clinical decisions on prosthesis selection.

References

[1] Vaduganathan, M., Mensah, G. A., Turco, J. V., Fuster, V., & Roth, G. A. (2022). The global burden of cardiovascular diseases and risk: a compass for future health. Journal of the American College of Cardiology, 80(25), 2361-2371. https://doi.org/10.1016/j.jacc.2022.11.005

[2] Polanczyk, A., Piechota-Polanczyk, A., Piastowska-Ciesielska, A. W., Huk, I., Neumayer, C., Wadowski, P. P., Balcer, J., & Strzelecki, M. (2024). Computational fluid dynamic as an engineering tool for the reconstruction of blood hemodynamics and spatial configuration before and after endoleak appearance. Metrology and Measurement Systems, 31(4) 711-731. https://doi.org/10.24425/mms.2024.152053

[3] Martin, S. S., Aday, A. W., Almarzooq, Z. I., Anderson, C. A., Arora, P., Avery, C. L., Baker-Smith, C. M., Gibbs, B. B., Beaton, A. Z., Boehme, A. K., Commodore-Mensah, Y., Currie, M. E., Elkind, M. S., Evenson, K. R., Generoso, G., Heard, D. G., Hiremath, S., Johansen, M. C., Kalani, R., & Palaniappan, L. P. (2024). 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data from the American Heart Association. Circulation, 149(8). https://doi.org/10.1161/cir.0000000000001209

[4] Kapuku, G. K., & Kop, W. J. (2022). Classification of Cardiovascular Diseases: Epidemiology, Diagnosis, and Treatment. In Springer eBooks (pp. 45-80). https://doi.org/10.1007/978-0-387-85960-6_3

[5] Polanczyk, A., Piechota-Polanczyk, A., Huk, I., Neumayer, C., Balcer, J., & Strzelecki, M. (2021). Computational fluid dynamic technique for assessment of how changing character of blood flow and different value of HCT influence blood hemodynamics in dissected aorta. Diagnostics, 11(10), 1866. https://doi.org/10.3390/diagnostics11101866

[6] Piché, M., Tchernof, A., & Després, J. (2020). Obesity phenotypes, diabetes, and cardiovascular diseases. Circulation Research, 126(11), 1477-1500. https://doi.org/10.1161/circresaha.120.316101

[7] Polanczyk, A., Piechota-Polanczyk, A., Stefańczyk, L., & Strzelecki, M. (2020). Spatial configuration of abdominal aortic aneurysm analysis as a useful tool for the estimation of stent-graft migration. Diagnostics, 10(10), 737. https://doi.org/10.3390/diagnostics10100737

[8] Boukhennoufa, N., Laamari, Y., & Benzid, R. (2024). Signal denoising using a low computational translation-invariant-like strategy involving multiple wavelet bases: application to synthetic and ECG signals. Metrology and Measurement Systems, 31(2), 259-278. https://doi.org/10.24425/mms.2024.148548

[9] Sadr, H., Salari, A., Ashoobi, M. T., & Nazari, M. (2024). Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models. European Journal of Medical Research, 29(1). https://doi.org/10.1186/s40001-024-02044-7

[10] Polanczyk, A., Piechota-Polanczyk, A., & Stefańczyk, L. (2017). A new approach for the pre-clinical optimization of a spatial configuration of bifurcated endovascular prosthesis placed in abdominal aortic aneurysms. PLoS ONE, 12(8), e0182717. https://doi.org/10.1371/journal.pone.0182717

[11] Duong, W., Grigorian, A., Yuen, S., Nahmias, N.-K., Kabutey, N., Farzaneh, C., Donayre, C., & Fujitani, R. M. (2023). Increased mortality in octogenarians undergoing endovascular aortic aneurysm repair for smaller aneurysms warrants caution. Annals of Vascular Surgery, 99, 175-185. https://doi.org/10.1016/j.avsg.2023.07.107

[12] Polanczyk, A., Piechota-Polanczyk, A., Piastowska-Ciesielska, A. W., Huk, I., Neumayer, C., Balcer, J., & Strzelecki, M. (2024). Reconstruction of the physiological behavior of real and synthetic vessels in controlled conditions. Applied Sciences, 14(6), 2600. https://doi.org/10.3390/app14062600

[13] Moise, K., Arun, K. M., Pillai, M., Salvador, J., Mehta, A. S., Goyal, Y., & Iruela-Arispe, M. L. (2024). Endothelial cell elongation and alignment in response to shear stress requires acetylation of microtubules. Frontiers in Physiology, 15. https://doi.org/10.3389/fphys.2024.1425620

[14] Carvalho, V., Gonçalves, I. M., Souza, A., Souza, M. S., Bento, D., Ribeiro, J. E., Lima, R., & Pinho, D. (2021). Manual and automatic image analysis segmentation methods for blood flow studies in microchannels. Micromachines, 12(3), 317. https://doi.org/10.3390/mi12030317

[15] Luijten, B., Chennakeshava, N., Eldar, Y. C., Mischi, M., & van Sloun, R. J. (2023). Ultrasound Signal processing: From models to deep learning. Ultrasound in Medicine & Biology, 49(3), 677-698. https://doi.org/10.1016/j.ultrasmedbio.2022.11.003

[16] Le, T. M., Paul, J. S., Al-Nashash, H., Tan, A., Luft, A. R., Sheu, F. S., & Ong, S. H. (2007). New Insights into Image Processing of Cortical Blood Flow Monitors Using Laser Speckle Imaging. IEEE Transactions on Medical Imaging, 26(6), 833-842. https://doi.org/10.1109/tmi.2007.892643

[17] Dumitrascu, O. M., & Qureshi, T. A. (2018). Retinal Vascular Imaging in Vascular Cognitive Impairment: Current and Future Perspectives. Journal of Experimental Neuroscience, 12. https://doi.org/10.1177/1179069518801291

[18] Du, C., Zhuang, J., & Huang, X. (2024). Deep learning technology in vascular image segmentation and disease diagnosis. Journal of Intelligent Medicine. https://doi.org/10.1002/jim4.15

[19] Abdulsahib, A. A., Mahmoud, M. A., Aris, H., Gunasekaran, S. S., & Mohammed, M. A. (2022). An automated image segmentation and useful feature extraction algorithm for retinal blood vessels in fundus images. Electronics, 11(9), 1295. https://doi.org/10.3390/electronics11091295

[20] Czepita, M., & Fabijańska, A. (2021). Image processing pipeline for the detection of blood flow through retinal vessels with subpixel accuracy in fundus images. Computer Methods and Programs in Biomedicine, 208, 106240. https://doi.org/10.1016/j.cmpb.2021.106240

[21] Khan, B., Riaz, Z., Ahmad, R. U. S., & Khoo, B. L. (2024). Advancements in wearable sensors for cardiovascular disease detection for health monitoring. Materials Science and Engineering R Reports, 159, 100804. https://doi.org/10.1016/j.mser.2024.100804

[22] Chrzanowski, L., Drozdz, J., Strzelecki, M., Krzeminska-Pakula, M., Jedrzejewski, K. S., & Kasprzak, J. D. (2007). Application of neural networks for the analysis of intravascular ultrasound and histological Aortic Wall Appearance - An In Vitro Tissue Characterization study. Ultrasound in Medicine & Biology, 34(1), 103-113. https://doi.org/10.1016/j.ultrasmedbio.2007.06.021

[23] Szmajda, M., Chyliński, M., Szacha, J., & Mroczka, J. (2023). Three methods for determining respiratory waves from ECG (Part I). Metrology and Measurement Systems, 30(4), 821-837. https://doi.org/10.24425/mms.2023.147956

[24] DeGroat, W., Abdelhalim, H., Patel, K., Mendhe, D., Zeeshan, S., & Ahmed, Z. (2024). Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-023-50600-8

[25] Mirani, B., Latifi, N., Lecce, M., Zhang, X., & Simmons, C. A. (2024). Biomaterials and biofabrication strategies for tissue-engineered heart valves. Matter, 7(9), 2896-2940. https://doi.org/10.1016/j.matt.2024.05.036

[26] Piola, M., Prandi, F., Bono, N., Soncini, M., Penza, E., Agrifoglio, M., Polvani, G., Pesce, M., & Fiore, G. B. (2013). A compact and automated ex vivo vessel culture system for the pulsatile pressure conditioning of human saphenous veins. Journal of Tissue Engineering and Regenerative Medicine, 10(3), E204-E215. https://doi.org/10.1002/term.1798

[27] Garoffolo, G., Ruiter, M. S., Piola, M., Brioschi, M., Thomas, A. C., Agrifoglio, M., Polvani, G., Coppadoro, L., Zoli, S., Saccu, C., Spinetti, G., Banfi, C., Fiore, G. B., Madeddu, P., Soncini, M., & Pesce, M. (2020). Coronary artery mechanics induces human saphenous vein remodelling via recruitment of adventitial myofibroblast-like cells mediated by Thrombospondin-1. Theranostics, 10(6), 2597-2611. https://doi.org/10.7150/thno.40595

[28] Polanczyk, A., Klinger, M., Nanobachvili, J., Huk, I., & Neumayer, C. (2018). Artificial circulatory model for analysis of human and artificial vessels. Applied Sciences, 8(7), 1017. https://doi.org/10.3390/app8071017

[29] Polanczyk, A., Podgorski, M., Polanczyk, M., Piechota-Polanczyk, A., Neumayer, C., & Stefanczyk, L. (2018). A novel patient-specific Human Cardiovascular System Phantom (HCSP) for reconstructions of pulsatile blood hemodynamic inside abdominal aortic aneurysm. IEEE Access, 6, 61896-61903. https://doi.org/10.1109/access.2018.2876377

[30] Polanczyk, A., Piechota-Polanczyk, A., Neumayer, C., & Huk, I. (2019). CFD Reconstruction of Blood Hemodynamics Based on a Self-made Algorithm in Patients with Acute Type IIIb Aortic Dissection Treated with TEVAR Procedure. In IUTAM Bookseries (pp. 75-84). https://doi.org/10.1007/978-3-030-13720-5_7

[31] Polanczyk, A., Podgorski, M., Polanczyk, M., Piechota-Polanczyk, A., Stefanczyk, L., & Strzelecki, M. (2019). A novel vision-based system for quantitative analysis of abdominal aortic aneurysm deformation. BioMedical Engineering OnLine, 18(1). https://doi.org/10.1186/s12938-019-0681-y

[32] Wang, G.-L., Xiao, Y., Voorhees, A., Qi, Y.-X., Jiang, Z.-L., & Han, H.-C. (2014). Artery remodeling under axial twist in three days organ culture. Annals of Biomedical Engineering, 43(8), 1738-1747. https://doi.org/10.1007/s10439-014-1215-1

[33] Niklason, L. E., & Lawson, J. H. (2020). Bioengineered human blood vessels. Science, 370(6513). https://doi.org/10.1126/science.aaw8682

[34] Choi, J., Lee, E. J., Jang, W. B., & Kwon, S.-M. (2023). Development of Biocompatible 3D-Printed Artificial Blood Vessels through Multidimensional Approaches. Journal of Functional Biomaterials, 14(10), 497. https://doi.org/10.3390/jfb14100497

[35] Mironov, V., Reis, N., & Derby, B. (2006). Review: Bioprinting: a Beginning. Tissue Engineering, 12(4), 631-634. https://doi.org/10.1089/ten.2006.12.631

[36] Vaz, V. M., & Kumar, L. (2021). 3D printing as a promising tool in personalized medicine. AAPS PharmSciTech, 22(1). https://doi.org/10.1208/s12249-020-01905-8

[37] Deshmane, S., Kendre, P., Mahajan, H., & Jain, S. (2021). Stereolithography 3D printing technology in pharmaceuticals: a review. Drug Development and Industrial Pharmacy, 47(9), 1362-1372. https://doi.org/10.1080/03639045.2021.1994990

[38] Yang, J., Li, H., Xu, L., & Wang, Y. (2021). Selective laser sintering versus conventional lost-wax casting for single metal copings: A systematic review and meta-analysis. Journal of Prosthetic Dentistry, 128(5), 897-904. https://doi.org/10.1016/j.prosdent.2021.02.011

[39] Charoo, N.A., Ali, S.F.B., Mohamed, E.M., Kuttolamadom, M.A., Ozkan, T., Khan, M.A., & Rahman, Z. (2020). Selective laser sintering 3D printing - an overview of the technology and pharmaceutical applications. Drug Development and Industrial Pharmacy, 46(6), 869-877. https://doi.org/10.1080/03639045.2020.1764027

[40] Zhang, Z., Huang, M., & Pan, X. (2020). Prosthetic reconstruction of superior vena cava system for thymic tumor: A retrospective analysis of 22 cases. The Thoracic and Cardiovascular Surgeon, 69(02), 165-172. https://doi.org/10.1055/s-0039-3401044

[41] Wang, X.-Y., Pei, Y., Xie, M., Jin, Z.-H., Xiao, Y.-S., Wang, Y., Zhang, L.-N., Li, Y., & Huang, W.-H. (2014). An artificial blood vessel implanted three-dimensional microsystem for modeling transvascular migration of tumor cells. Lab on a Chip, 15(4), 1178-1187. https://doi.org/10.1039/c4lc00973h

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Published

2025-09-30

How to Cite

Polanczyk, Andrzej, et al. “Measurement of Mechanical Properties of Synthetic Vessels Using a Man-Made Circulatory Model”. Metrology and Measurement Systems, vol. 32, no. 3, Sept. 2025, pp. art. no. 16, 1-16, doi:10.24425/mms.2025.155805.

Issue

Vol. 32 No. 3 (2025)

Section

Articles

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