Combining 4D Flow MRI and Complex Networks Theory to Characterize the Hemodynamic Heterogeneity in Dilated and Non-dilated Human Ascending Aortas

Other authors

Institut Català de la Salut

[Calò K, Gallo D, Scarsoglio S, Ridolfi L] PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy. [Guala A] Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. CIBER-CV, Instituto de Salud Carlos III (ISCIII), Madrid, Spain. [Rodriguez Palomares J] Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain. CIBER-CV, Instituto de Salud Carlos III (ISCIII), Madrid, Spain. Servei de Cardiologia, Vall d’Hebron Hospital Universitari, Barcelona, Spain

Vall d'Hebron Barcelona Hospital Campus

Publication date

2022-02-22T07:22:00Z

2022-02-22T07:22:00Z

2021-09



Abstract

Dilatació aòrtica; Aneurisma de l'aorta ascendent; Anàlisi espai-temporal


Dilatación aórtica; Aneurisma de la aorta ascendente; Análisis espacio-temporal


Aortic dilation; Ascending aorta aneurysm; Spatiotemporal analysis


Motivated by the evidence that the onset and progression of the aneurysm of the ascending aorta (AAo) is intertwined with an adverse hemodynamic environment, the present study characterized in vivo the hemodynamic spatiotemporal complexity and organization in human aortas, with and without dilated AAo, exploring the relations with clinically relevant hemodynamic and geometric parameters. The Complex Networks (CNs) theory was applied for the first time to 4D flow magnetic resonance imaging (MRI) velocity data of ten patients, five of them presenting with AAo dilation. The time-histories along the cardiac cycle of velocity-based quantities were used to build correlation-based CNs. The CNs approach succeeded in capturing large-scale coherent flow features, delimiting flow separation and recirculation regions. CNs metrics highlighted that an increasing AAo dilation (expressed in terms of the ratio between the maximum AAo and aortic root diameter) disrupts the correlation in forward flow reducing the correlation persistence length, while preserving the spatiotemporal homogeneity of secondary flows. The application of CNs to in vivo 4D MRI data holds promise for a mechanistic understanding of the spatiotemporal complexity and organization of aortic flows, opening possibilities for the integration of in vivo quantitative hemodynamic information into risk stratification and classification criteria.


Open access funding provided by Politecnico di Torino within the CRUI-CARE Agreement.

Document Type

Article


Published version

Language

English

Publisher

Springer

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Annals of Biomedical Engineering;49(9)

https://doi.org/10.1007/s10439-021-02798-9

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Rights

Attribution 4.0 International

http://creativecommons.org/licenses/by/4.0/

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