journal article·2020-04-01Open access

A clinically aligned experimental approach for quantitative characterization of patient-specific cardiovascular models

Ana Paula Narata, Fernando Silva de Moura, Fréderic Patat, Alberto Marzo, Ignacio Larrabide, Jean-Marc Gregoire, Cecile Perrault, Charles A. Sennoga, Ayache Bouakaz

AIP Advances· Volume 10· Issue 4

Abstract

Recent improvements in computational tools opened the possibility of patient-specific modeling to aid clinicians during diagnosis, treatment, and monitoring. One example is the modeling of blood flow for surgical planning, where modeling can help predict the prognosis. Computational analysis is used to extract hemodynamic information about the case; however, these methods are sensitive to assumptions on blood properties, boundary conditions, and appropriate geometry accuracy. When available, experimental measurements can be used to validate the results and, among the modalities, ultrasound-based methods are suitable due to their relative low cost and non-invasiveness. This work proposes a procedure to create accurate patient-specific silicone replicas of blood vessels and a power Doppler compatible experimental setup able to simulate and measure realistic flow conditions. The assessment of silicone model geometry shows small discrepancies between these and the target geometries (median of surface error lies within 57 µm and 82 μm). Power Doppler measurements were compared against computational fluid dynamics results, showing discrepancies within 10% near the wall. The experimental approach offers a setup to quantify flow in in vitro systems and provide more accurate results where other techniques (e.g., particle image velocimetry and particle tracking velocimetry) have shown limitations due to the interference of the interface.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Cardiovascular Health and Disease Prevention · Cardiovascular Function and Risk Factors · Ultrasound Imaging and Elastography

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

UMR 1253, iBrain, Université de Tours, Inserm 1 , 37032 Tours, France

Engineering, Modeling and Applied Social Sciences Center, Universidade Federal do ABC 2 , 09606-070 São Paulo, Brazil

Mechanical Engineering Department, INSIGNEO Institute for in silico Medicine, University of Sheffield 3 , S1 3JD Sheffield, United Kingdom

PLADEMA-CONICET, Universidad Nacional del Centro de la Provincia de Buenos Aires 4 , Pinto 399, CP 7000 Tandil, Argentina

Funding

Fondo para la Investigación Científica y Tecnológica · Royal Academy of Engineering

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