Otta, M. orcid.org/0000-0002-8062-1354, Zajac, K. orcid.org/0000-0003-1393-8236, Malawski, M. orcid.org/0000-0001-6005-0243 et al. (4 more authors) (2025) Shape vs flow: a 2D statistical shape analysis of the projection of common iliac veins in patients with deep vein thrombosis. In: Wachinger, C., Luijten, G., Elhabian, S., Gopinath, K. and Egger, J., (eds.) UNSPECIFIED Shape in Medical Imaging (ShapeMI 2025), 23-27 Sep 2025, Daejeon, South Korea. Lecture Notes in Computer Science, 16171. Springer Nature Switzerland, pp. 292-303. ISBN: 9783032067739. ISSN: 0302-9743. EISSN: 1611-3349.
Abstract
Deep vein thrombosis (DVT) of the lower limb is characterised by the formation of abnormal blood clots in deep veins of lower extremity. Changes in blood flow have been associated with an increased risk of thrombus development. Understanding the relationship between variable venous anatomy and haemodynamics can reveal insights to support clinical decision-making processes. The purpose of this study was to combine statistical shape modelling (SSM)- to analyse venous shape- and computational fluid dynamics (CFD)- to estimate blood flowin the common iliac vein to demonstrate the feasibility of a combined framework to support the treatment of DVT. Principal geodesic analysis was used to extract dominant shape modes from a set of 24 venous shapes in 2D: 8 patient-specific extracted from standard angiograms and 16 synthetic complementing the set. Steady-state CFD simulations were run on the associated 3D geometries. Low wall shear stress distributions below three thresholds (< 0.15,< 0.10,< 0.05Pa) were the haemodynamic risk metrics of choice. The distribution of CFD output metrics was evaluated using the three most dominant shape modes from PGA and compared to the three modes that showed the strongest correlation with the CFD metrics, illustrating that they are not the same. The study demonstrated the feasibility of combining SSM and CFD to examine the importance of shape variability and inflow in a local region of the venous circulation. It will serve as a basis for extended work on a larger set of venous shapes extracted from standard medical images
Metadata
| Item Type: | Proceedings Paper |
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| Copyright, Publisher and Additional Information: | © 2025 The Authors. Except as otherwise noted, this author-accepted version of a paper published in Shape in Medical Imaging is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | statistical shape modelling; computational fluid dynamics; deep vein thrombosis |
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| Institution: | The University of Sheffield |
| Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Medicine and Population Health |
| Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 857533 |
| Date Deposited: | 21 Oct 2025 09:35 |
| Last Modified: | 21 Oct 2025 09:36 |
| Status: | Published |
| Publisher: | Springer Nature Switzerland |
| Series Name: | Lecture Notes in Computer Science |
| Refereed: | Yes |
| Identification Number: | 10.1007/978-3-032-06774-6_22 |
| Related URLs: | |
| Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:233275 |

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