Literature DB >> 32479874

Computational Fluid Dynamics Modeling of Proximal Landing Zones for Thoracic Endovascular Aortic Repair in the Bovine Arch Variant.

Massimiliano M Marrocco-Trischitta1, Rodrigo M Romarowski2, Moad Alaidroos3, Francesco Sturla2, Mattia Glauber4, Giovanni Nano5.   

Abstract

BACKGROUND: To assess the endograft displacement forces (DFs), which quantify the forces exerted by the pulsatile blood flow on the vessel wall and transmitted on the terminal fixation site of the endograft after its deployment in proximal landing zones (PLZs) of the bovine aortic arch variant.
METHODS: Thirty healthy aortic computed tomographic angiographies of subjects with bovine arch configuration (10 per type of arch, I-III) were selected for the purpose of the study. A 3-dimensional model of the aortic arch lumen was reconstructed. Computational fluid dynamics modeling was then used to compute DF magnitude and orientation (i.e., x, y, and z axes) in PLZs of each case. DF values were normalized to the corresponding aortic wall area to estimate equivalent surface traction (EST).
RESULTS: DFs were highest in zone 0, consistently with the greater surface area. DFs in zone 3 were much greater than in zone 2 because of a 3-fold greater upward component (z axis) (P < 0.001), being therefore mainly oriented orthogonally to the aortic blood flow and to the vessel longitudinal axis in that zone. EST progressively increased from zone 0 toward more distal PLZs, with EST in zone 3 being much greater than that in zone 2 (P < 0.001). The same pattern was observed after stratification by type of arch.
CONCLUSIONS: The bovine arch is associated with a consistent fluid dynamic pattern, which identifies in zone 3 an unfavorable biomechanical environment for endograft deployment.
Copyright © 2020 Elsevier Inc. All rights reserved.

Entities:  

Year:  2020        PMID: 32479874     DOI: 10.1016/j.avsg.2020.05.024

Source DB:  PubMed          Journal:  Ann Vasc Surg        ISSN: 0890-5096            Impact factor:   1.466


  1 in total

1.  A Deep Learning-Based and Fully Automated Pipeline for Thoracic Aorta Geometric Analysis and Planning for Endovascular Repair from Computed Tomography.

Authors:  Simone Saitta; Francesco Sturla; Alessandro Caimi; Alessandra Riva; Maria Chiara Palumbo; Giovanni Nano; Emiliano Votta; Alessandro Della Corte; Mattia Glauber; Dante Chiappino; Massimiliano M Marrocco-Trischitta; Alberto Redaelli
Journal:  J Digit Imaging       Date:  2022-01-26       Impact factor: 4.056

  1 in total

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