Abstract Over the past few years, there has been an increase of clinical interest aimed at looking for correlations between morphology, extracted through statistical shape models (SSMs), and hemodynamics, extracted through computational fluid dynamics (CFD) simulations, in cardiovascular diseases. This study explores correlations between aortic morphology and hemodynamics in the thoracic aorta (TA). Existing research often simplifies geometries by excluding supra-aortic vessels due to software limitations in non-rigid registration. To overcome this, a novel algorithm was used to include these vessels in TA analysis. Principal component analysis reduced dimensionality, followed by automatic CFD simulations and correlation analysis between geometric and hemodynamic parameters. The first (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\varvec{\mathcal {M}}_{0}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="bold-script">M</mi> </mrow> <mn>0</mn> </msub> </math></EquationSource> </InlineEquation>) and second (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">$\varvec{\mathcal {M}}_{1}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="bold-script">M</mi> </mrow> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation>) SSM modes explained 46.9<InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\%$</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> and 22.4<InlineEquation ID="IEq4"> <EquationSource Format="TEX">$\%$</EquationSource> <EquationSource Format="MATHML"><math> <mo>%</mo> </math></EquationSource> </InlineEquation> of dataset variance, respectively. Significant correlations were identified between <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$\varvec{\mathcal {M}}_{0}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="bold-script">M</mi> </mrow> <mn>0</mn> </msub> </math></EquationSource> </InlineEquation> and ascending TA aneurysm volume (Pr = 0.69), and <InlineEquation ID="IEq6"> <EquationSource Format="TEX">$\varvec{\mathcal {M}}_{1}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="bold-script">M</mi> </mrow> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> and TA tortuosity (Pr = 0.60). Ten TA shapes were generated by varying standard deviations of <InlineEquation ID="IEq7"> <EquationSource Format="TEX">$\varvec{\mathcal {M}}_{0}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="bold-script">M</mi> </mrow> <mn>0</mn> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq8"> <EquationSource Format="TEX">$\varvec{\mathcal {M}}_{1}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mrow> <mi mathvariant="bold-script">M</mi> </mrow> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> from −2 to +2, and CFD simulations revealed links between wall shear stress (WSS) indicators and TA morphology. This study presents a novel pipeline to analyze geometric and hemodynamic correlations using realistic TA geometries generated via SSM. Graphical abstract

Effects of vessel morphology on aortic hemodynamics: a statistical shape and CFD investigation

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发表日期：

2025年10月7日

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Marilena Mazzoli,
Katia Capellini,
Simona Celi

摘要

Abstract

Over the past few years, there has been an increase of clinical interest aimed at looking for correlations between morphology, extracted through statistical shape models (SSMs), and hemodynamics, extracted through computational fluid dynamics (CFD) simulations, in cardiovascular diseases. This study explores correlations between aortic morphology and hemodynamics in the thoracic aorta (TA). Existing research often simplifies geometries by excluding supra-aortic vessels due to software limitations in non-rigid registration. To overcome this, a novel algorithm was used to include these vessels in TA analysis. Principal component analysis reduced dimensionality, followed by automatic CFD simulations and correlation analysis between geometric and hemodynamic parameters. The first ( $\varvec{\mathcal {M}}_{0}$ $M_{0}$ ) and second ( $\varvec{\mathcal {M}}_{1}$ $M_{1}$ ) SSM modes explained 46.9 $\%$ $%$ and 22.4 $\%$ $%$ of dataset variance, respectively. Significant correlations were identified between $\varvec{\mathcal {M}}_{0}$ $M_{0}$ and ascending TA aneurysm volume (Pr = 0.69), and $\varvec{\mathcal {M}}_{1}$ $M_{1}$ and TA tortuosity (Pr = 0.60). Ten TA shapes were generated by varying standard deviations of $\varvec{\mathcal {M}}_{0}$ $M_{0}$ and $\varvec{\mathcal {M}}_{1}$ $M_{1}$ from −2 to +2, and CFD simulations revealed links between wall shear stress (WSS) indicators and TA morphology. This study presents a novel pipeline to analyze geometric and hemodynamic correlations using realistic TA geometries generated via SSM.

Graphical abstract