Spatio-temporal Image Correlation Analysis for 3D Flow Field Mapping in Microfluidic Devices.

Microfluidic devices reproducing 3D networks are particularly valuable for nanomedicine applications such as tissue engineering and active cell sorting. There is however a gap in the possibility to measure how the flow evolves in such 3D structures. We show here that it is possible to map 3D flows in complex microchannel networks by combining wide field illumination to image correlation approaches. For this purpose, we have derived the spatiotemporal image correlation analysis of time stacks of single-plane illumination microscopy images. From the detailed analytical and numerical analysis of the resulting model, we developed a fitting method that allows us to measure, besides the in-plane velocity, the out-of-plane velocity component down to v(z) congruent to 65 mu m/s. We have applied this method successfully to the 3D reconstruction of flows in microchannel networks with planar and 3D ramifications. These different network architectures have been realized by exploiting the great prototyping ability of a 3D printer, whose precision can reach few tens of micrometers, coupled to poly dimethyl-siloxane soft-printing lithography.