English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Numerical simulations of blood flow are a valuable tool to investigate thepathophysiology of ascending thoracic aortic aneurysms (ATAA). To accuratelyreproduce hemodynamics, computational fluid dynamics (CFD) models must employrealistic inflow boundary conditions (BCs). However, the limited availabilityof in vivo velocity measurements still makes researchers resort to idealizedBCs. In this study we generated and thoroughly characterized a large dataset ofsynthetic 4D aortic velocity profiles suitable to be used as BCs for CFDsimulations. 4D flow MRI scans of 30 subjects with ATAA were processed toextract cross-sectional planes along the ascending aorta, ensuring spatialalignment among all planes and interpolating all velocity fields to a referenceconfiguration. Velocity profiles of the clinical cohort were extensivelycharacterized by computing flow morphology descriptors of both spatial andtemporal features. By exploiting principal component analysis (PCA), astatistical shape model (SSM) of 4D aortic velocity profiles was built and adataset of 437 synthetic cases with realistic properties was generated.Comparison between clinical and synthetic datasets showed that the syntheticdata presented similar characteristics as the clinical population in terms ofkey morphological parameters. The average velocity profile qualitativelyresembled a parabolic-shaped profile, but was quantitatively characterized bymore complex flow patterns which an idealized profile would not replicate.Statistically significant correlations were found between PCA principal modesof variation and flow descriptors. We built a data-driven generative model of4D aortic velocity profiles, suitable to be used in computational studies ofblood flow. The proposed software system also allows to map any of thegenerated velocity profiles to the inlet plane of any virtual subject given itscoordinate set.

Data-driven generation of 4D velocity profiles in the aneurysmal ascending aorta