Torrisi Federica, Stella Giovanna, Guarino Francesca M, Bucolo Maide
Department of Electrical, Electronic and Computer Engineering, University of Catania, 95125 Catania, Italy.
Department of Biomedical and Biotechnological Sciences, University of Catania, 95125 Catania, Italy.
Biomicrofluidics. 2023 Jan 23;17(1):014105. doi: 10.1063/5.0138587. eCollection 2023 Jan.
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 to ), 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.
本文提出了一种细胞计数算法和基于数字粒子图像测速（DPIV）方法的速度算法相结合的方法，以研究微粒在流体动力刺激下的集体行为。使用了不同性质和直径（从 到 ）的微粒，如活细胞和二氧化硅珠，进行了广泛的实验。将生物流体在外部振荡流的作用下注入微通道的入口，并通过电荷耦合器件（CCD）相机和光电探测器同时在研究区域内对该过程进行监测。所提出的数据分析程序基于基于DPIV的算法来推断微粒速度，并基于自定义计数算法来获取瞬时微粒数量。计数算法很容易与基于DPIV的算法集成，以便自动对不同的视频进行分析，并在时域和频域中对结果进行后处理。所进行的实验突出了微粒对外部刺激的流体动力响应的差异，以及将它们与微粒物理性质相关联的可能性。此外，为了克服开发实时方法对硬件和软件的要求，还研究了使用光电探测器信号检测流动作为相机采集替代方案的可能性。将光电探测器信号与速度趋势进行比较，作为进一步简化和加快数据采集与分析的概念验证。该算法的灵活性突出了所提出方法适用于嵌入式系统实时检测的潜力。
