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RESEARCH PRODUCT
Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms
Chiara CatalanoValentina AgneseGiovanni GentileGiuseppe M. RaffaMichele PilatoSalvatore Pastasubject
Fluid Flow and Transfer ProcessesTechnologyQH301-705.5Process Chemistry and TechnologyTPhysicsQC1-999General Engineeringstatistical shape analysisSettore ING-IND/34 - Bioingegneria IndustrialeEngineering (General). Civil engineering (General)Computer Science Applicationscomputational fluid dynamicChemistryascending aortic aneurysmcardiovascular systemGeneral Materials Scienceascending aortic aneurysm; statistical shape analysis; computational fluid dynamicAscending aortic aneurysm Computational fluid dynamic Statistical shape analysisTA1-2040Biology (General)InstrumentationQD1-999description
Atlas-based analyses of patients with cardiovascular diseases have recently been explored to understand the mechanistic link between shape and pathophysiology. The construction of probabilistic atlases is based on statistical shape modeling (SSM) to assess key anatomic features for a given patient population. Such an approach is relevant to study the complex nature of the ascending thoracic aortic aneurysm (ATAA) as characterized by different patterns of aortic shapes and valve phenotypes. This study was carried out to develop an SSM of the dilated aorta with both bicuspid aortic valve (BAV) and tricuspid aortic valve (TAV), and then assess the computational hemodynamic of virtual models obtained by the deformation of the mean template for specific shape boundaries (i.e., ±1.5 standard deviation, σ). Simulations demonstrated remarkable changes in the velocity streamlines, blood pressure, and fluid shear stress with the principal shape modes such as the aortic size (Mode 1), vessel tortuosity (Mode 2), and aortic valve morphologies (Mode 3). The atlas-based disease assessment can represent a powerful tool to reveal important insights on ATAA-derived hemodynamic, especially for aneurysms which are considered to have borderline anatomies, and thus challenging decision-making. The utilization of SSMs for creating probabilistic patient cohorts can facilitate the understanding of the heterogenous nature of the dilated ascending aorta.
year | journal | country | edition | language |
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2021-12-31 | Applied Sciences; Volume 12; Issue 1; Pages: 394 |