Torrisi F, Stella G, Guarino FM, Bucolo M. Cell counting and velocity algorithms for hydrodynamic study of unsteady biological flows in micro-channels.
BIOMICROFLUIDICS 2023;
17:014105. [PMID:
36714795 PMCID:
PMC9878589 DOI:
10.1063/5.0138587]
[Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 12/20/2022] [Indexed: 05/20/2023]
Abstract
In this paper, the combination of two algorithms, a cell counting algorithm and a velocity algorithm based on a Digital Particle Image Velocimetry (DPIV) method, is presented to study the collective behavior of micro-particles in response to hydrodynamic stimuli. A wide experimental campaign was conducted using micro-particles of different natures and diameters (from 5 to 16 μ m ), such as living cells and silica beads. The biological fluids were injected at the inlet of a micro-channel with an external oscillating flow, and the process was monitored in an investigated area, simultaneously, through a CCD camera and a photo-detector. The proposed data analysis procedure is based on the DPIV-based algorithm to extrapolate the micro-particles velocities and a custom counting algorithm to obtain the instantaneous micro-particles number. The counting algorithm was easily integrated with the DPIV-based algorithm, to automatically run the analysis to different videos and to post-process the results in time and frequency domain. The performed experiments highlight the difference in the micro-particles hydrodynamic responses to external stimuli and the possibility to associate them with the micro-particles physical properties. Furthermore, in order to overcome the hardware and software requirements for the development of a real-time approach, it was also investigated the possibility to detect the flows by photo-detector signals as an alternative to camera acquisition. The photo-detector signals were compared with the velocity trends as a proof of concept for further simplification and speed-up of the data acquisition and analysis. The algorithm flexibility underlines the potential of the proposed methodology to be suitable for real-time detection in embedded systems.
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