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Atlas-Based Evaluation of Hemodynamic in Ascending Thoracic Aortic Aneurysms

Authors :
Chiara Catalano
Valentina Agnese
Giovanni Gentile
Giuseppe M. Raffa
Michele Pilato
Salvatore Pasta
Source :
Applied Sciences, Vol 12, Iss 1, p 394 (2021)
Publication Year :
2021
Publisher :
MDPI AG, 2021.

Abstract

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.

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.fc143552af9e41f8b41f2f39c64d7fc4
Document Type :
article
Full Text :
https://doi.org/10.3390/app12010394