Home LiteratureArticle Details
PMID: 42061168 Published · ppublish English

MRI-based CFD simulations of transient blood flow in compliant aortas using the LDDMM framework.

Lin D, Westenberg J, Lamb H, Kenjereš S

Abstract

Traditional CFD analyses often rely on static (rigid) vascular geometries, which neglect the physiologically relevant motion of the aortic wall. This simplification can lead to inaccuracies in estimating key hemodynamic biomarkers, such as wall shear stress (WSS) and oscillatory shear index (OSI). This study introduces the Large Deformation Diffeomorphic Metric Mapping (LDDMM) method to enable computationally efficient simulations of transient blood flow in compliant, subject- and patient-specific aortas derived from 4D Flow MRI data. The proposed framework simplifies CFD pre-processing, improves morphing accuracy, and enables physiologically realistic motion of the thoracic aorta, including its side-branches. The method was applied to two aortic geometries: a healthy case (HC) and a case with thoracic aortic aneurysm (TAA) located in the ascending region. The results were compared with those obtained from fixed aortic geometries extracted at peak systole. Hemodynamic biomarkers showed significant differences between static and moving geometries. For the healthy case (HC), the differences were 18% for the time-averaged wall shear stress (TAWSS) and 46% for the oscillatory shear index (OSI). For the thoracic aorta aneurysm (TAA) case, the corresponding values were 14% and 47%, respectively. These findings highlight the importance of incorporating aortic wall motion in hemodynamic simulations. The developed LDDMM-based framework can be readily extended to other imaging modalities, such as ultrasound or computed tomography, and is recommended for future CFD analyses of compliant aortas.

Keywords
Computational fluid dynamics (CFD) LDDMM Moving aortic wall simulation Shape registration Thoracic aortic aneurysm
Article Info
Journal
Computer methods and programs in biomedicine
Abbr.
Comput Methods Programs Biomed
ISSN
1872-7565
Published
2026-08-01
Language
English
Country/Region
Ireland
NLM ID
8506513
Analysis Services
Analysis Services

Contact

No. 2 Wenbo Road, Zhangqiu District, Jinan, Shandong

Qilu Normal University · Genelibs Bioinformatics Lab

750 Shunhua Rd, Jinan

2F, Bldg F, University Science Park

Tel: 0531-88819269

WeChat Official Account

Follow our WeChat subscription account for real-time updates and the latest in medical and biological research.


Business Email

E-mail: [email protected]