2022
DOI: 10.3389/fbioe.2022.1011806
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Smartphone-based particle image velocimetry for cardiovascular flows applications: A focus on coronary arteries

Abstract: An experimental set-up is presented for the in vitro characterization of the fluid dynamics in personalized phantoms of healthy and stenosed coronary arteries. The proposed set-up was fine-tuned with the aim of obtaining a compact, flexible, low-cost test-bench for biomedical applications. Technically, velocity vector fields were measured adopting a so-called smart-PIV approach, consisting of a smartphone camera and a low-power continuous laser (30 mW). Experiments were conducted in realistic healthy and steno… Show more

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Cited by 7 publications
(2 citation statements)
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“…The movement of particles within the physical space, as captured in the image frames, is notably influenced by the acquisition frame rate. Subsequently, these particles motions undergo analysis through image velocimetry, as highlighted in the study by Caridi et al [35]. Consequently, a comparative examination was conducted using distinct image frequencies-specifically, 24 fps, 12 fps, and 6 fps-aimed at determining the image frequencies that yield the most suitable results.…”
Section: Influence Of Image Frequency On Surface Flow Measurementmentioning
confidence: 99%
“…The movement of particles within the physical space, as captured in the image frames, is notably influenced by the acquisition frame rate. Subsequently, these particles motions undergo analysis through image velocimetry, as highlighted in the study by Caridi et al [35]. Consequently, a comparative examination was conducted using distinct image frequencies-specifically, 24 fps, 12 fps, and 6 fps-aimed at determining the image frequencies that yield the most suitable results.…”
Section: Influence Of Image Frequency On Surface Flow Measurementmentioning
confidence: 99%
“…However, it's crucial to note that this popular fully supervised learning paradigm falls short in addressing several challenges associated with specific scientific domains involving turbulence flow analysis cases, such as cardiovascular disease research [27], meteorological studies of air currents in severe weathers [28], tracking the spread of pollutants in natural waterways [29] and so on. The challenges primarily focus on two dimensions: data volume requirements and cross-domain robustness.…”
mentioning
confidence: 99%