{"title":"Computational Fluid Dynamics Methodology for Aortic Aneurysm Analysis in Computed Tomography (CT) Datasets.","authors":"Charles G Jenkinson, Tristan L Wood","doi":"10.7759/cureus.84523","DOIUrl":null,"url":null,"abstract":"<p><p>Aortic aneurysms present significant clinical challenges due to the risk of rupture associated with the abnormal dilation of the aorta. Computational fluid dynamics (CFD) analysis is an emerging, non-invasive method to analyse haemodynamic forces within aneurysmal regions. We present a detailed, reproducible workflow for the CFD analysis of aortic aneurysms based on cardiac-gated computed tomography (CT) data. Using a structured toolchain of open-source software, namely, Horos (Horos Project, Annapolis, MD, USA) for image preparation, Image Tool Kit-SNAP (ITK-SNAP) (University of Pennsylvania, Philadelphia, PA, USA) for segmentation, MeshLab (Istituto di Scienza e Tecnologie dell'Informazione-Consiglio Nazionale delle Ricerche (ISTI-CNR), Pisa, Italy) for mesh refinement, Blender (Blender Foundation, Amsterdam, Netherlands, https://www.blender.org) for boundary patching, OpenFOAM (OpenFOAM Foundation, London, UK) for CFD simulation, ParaView (Kitware, Inc., Clifton Park, NY, USA) for visualisation, and R (R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/) for statistical analysis, the methodology achieves high fidelity in modeling patient-specific flow conditions. Key stages of the workflow address segmentation accuracy, mesh quality, and boundary condition assignment, ensuring that the model captures physiological flow characteristics. This approach provides a valuable and accessible tool for clinicians and researchers, supporting assessments of haemodynamic risk factors in cardiovascular research. Our model aims to provide insights into wall shear stress (WSS), pressure distributions, and flow dynamics that may contribute to aneurysm progression and high-risk features.</p>","PeriodicalId":93960,"journal":{"name":"Cureus","volume":"17 5","pages":"e84523"},"PeriodicalIF":1.0000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12098751/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cureus","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7759/cureus.84523","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
引用次数: 0
Abstract
Aortic aneurysms present significant clinical challenges due to the risk of rupture associated with the abnormal dilation of the aorta. Computational fluid dynamics (CFD) analysis is an emerging, non-invasive method to analyse haemodynamic forces within aneurysmal regions. We present a detailed, reproducible workflow for the CFD analysis of aortic aneurysms based on cardiac-gated computed tomography (CT) data. Using a structured toolchain of open-source software, namely, Horos (Horos Project, Annapolis, MD, USA) for image preparation, Image Tool Kit-SNAP (ITK-SNAP) (University of Pennsylvania, Philadelphia, PA, USA) for segmentation, MeshLab (Istituto di Scienza e Tecnologie dell'Informazione-Consiglio Nazionale delle Ricerche (ISTI-CNR), Pisa, Italy) for mesh refinement, Blender (Blender Foundation, Amsterdam, Netherlands, https://www.blender.org) for boundary patching, OpenFOAM (OpenFOAM Foundation, London, UK) for CFD simulation, ParaView (Kitware, Inc., Clifton Park, NY, USA) for visualisation, and R (R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/) for statistical analysis, the methodology achieves high fidelity in modeling patient-specific flow conditions. Key stages of the workflow address segmentation accuracy, mesh quality, and boundary condition assignment, ensuring that the model captures physiological flow characteristics. This approach provides a valuable and accessible tool for clinicians and researchers, supporting assessments of haemodynamic risk factors in cardiovascular research. Our model aims to provide insights into wall shear stress (WSS), pressure distributions, and flow dynamics that may contribute to aneurysm progression and high-risk features.