Real-time monitoring system for microfluidics

A new non-invasive real-time system for the monitoring and control of microfluidodynamic phenomena is proposed. The general purpose design of such system is suitable for in vitro and in vivo experimental setup and therefore for microfluidic application in the biomedical field such as lab-on-chip and for research studies in the field of microcirculation. The system consists of an ad hoc optical setup for image magnification providing images suitable for image acquisition and processing. The optic system was designed and developed using discrete opto-mechanic components mounted on a breadboard in order to provide an optic path accessible at any point where the information needs to be acquired. The optic sensing, acquisition, and processing were performed using an integrated vision system based on the Cellular Nonlinear Networks (CNNs) analogic technology called Focal Plane Processor (FPP, Eye-RIS, Anafocus) and inserted in the optic path. Ad hoc algorithms were implemented for the real-time analysis and extraction of fluido-dynamic parameters in micro-channels. They were tested on images recorded during in vivo microcirculation experiments on hamsters and then they were applied on images optically acquired and processed in real-time during in vitro experiments on a continuous microfluidic device (serpentine mixer, ThinXXS) with a two-phase fluid.

[1]  Luigi Fortuna,et al.  Image processing for medical diagnosis using CNN , 2003 .

[2]  Tamás Roska,et al.  CNN‐based spatio‐temporal nonlinear filtering and endocardial boundary detection in echocardiography , 1999, Int. J. Circuit Theory Appl..

[3]  K. Messmer,et al.  Technical report—a new chamber technique for microvascular studies in unanesthetized hamsters , 1980, Research in experimental medicine. Zeitschrift fur die gesamte experimentelle Medizin einschliesslich experimenteller Chirurgie.

[4]  H. Klein,et al.  Oxygen carriers and transfusion medicine. , 1994, Artificial cells, blood substitutes, and immobilization biotechnology.

[5]  P Arena,et al.  A cellular nonlinear network: real-time technology for the analysis of microfluidic phenomena in blood vessels , 2006, Nanotechnology.

[6]  Luigi Fortuna,et al.  Cellular non‐linear networks for microcirculation applications , 2006, Int. J. Circuit Theory Appl..

[7]  Lin-Bao Yang,et al.  Cellular neural networks: theory , 1988 .

[8]  P Arena,et al.  Cellular neural networks for real-time DNA microarray analysis. , 2002, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[9]  Luigi Fortuna,et al.  An object oriented segmentation on analog CNN chip , 2003 .