Smartphone-based particle tracking velocimetry for the in vitro assessment of coronary flows.

The present study adopts a smartphone-based approach for the experimental characterization of coronary flows. Technically, Particle Tracking Velocimetry (PTV) measurements were performed using a smartphone camera and a low-power continuous wave laser in realistic healthy and stenosed phantoms of left anterior descending artery with inflow Reynolds numbers approximately ranging from 20 to 200. A Lagrangian-Eulerian mapping was performed to convert Lagrangian PTV velocity data to a Eulerian grid. Eulerian velocity and vorticity data obtained from smartphone-based PTV measurements were compared with Particle Image Velocimetry (PIV) measurements performed with a smartphone-based setup and with a conventional setup based on a high-power double-pulsed laser and a CMOS camera. Smartphone-based PTV and PIV velocity flow fields substantially agreed with conventional PIV measurements, with the former characterized by lower average percentage differences than the latter. Discrepancies emerged at high flow regimes, especially at the stenosis throat, due to particle image blur generated by smartphone camera shutter speed and image acquisition frequency. In conclusion, the present findings demonstrate the feasibility of PTV measurements using a smartphone camera and a low-power light source for the in vitro characterization of cardiovascular flows for research, industrial and educational purposes, with advantages in terms of costs, safety and usability.

Smartphone-based particle image velocimetry for cardiovascular flows applications: A focus on coronary arteries.

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 stenosed 3D-printed phantoms of left anterior descending coronary artery reconstructed from angiographic images. Time resolved image acquisition was made possible by the combination of the image acquisition frame rate of last generation commercial smartphones and the flow regimes characterizing coronary hemodynamics (velocities in the order of 10 cm/s). Different flow regimes (Reynolds numbers ranging from 20 to 200) were analyzed. The smart-PIV approach was able to provide both qualitative flow visualizations and quantitative results. A comparison between smart-PIV and conventional PIV (i.e., the gold-standard experimental technique for bioflows characterization) measurements showed a good agreement in the measured velocity vector fields for both the healthy and the stenosed coronary phantoms. Displacement errors and uncertainties, estimated by applying the particle disparity method, confirmed the soundness of the proposed smart-PIV approach, as their values fell within the same range for both smart and conventional PIV measured data (≈5% for the normalized estimated displacement error and below 1.2 pixels for displacement uncertainty). In conclusion, smart-PIV represents an easy-to-implement, low-cost methodology for obtaining an adequately robust experimental characterization of cardiovascular flows. The proposed approach, to be intended as a proof of concept, candidates to become an easy-to-handle test bench suitable for use also outside of research labs, e.g., for educational or industrial purposes, or as first-line investigation to direct and guide subsequent conventional PIV measurements.