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Multiple exposure speckle imaging (MESI) allows to map relative blood flows at the surface of biological tissues. MESI is an extension of laser speckle contrast imaging (LSCI). It relies on the computation of speckle contrast K for several exposure times T, allowing to discriminate the contribution of static scatters (bulk tissues) and moving scatterers (red blood cells). The MESI model describes K(T) as a function of tc, rho, beta, and v. These variables are respectively the decorrelation time of the moving scatterers, the relative contribution of static scatterers to the speckle pattern, a normalization factor for the imaging parameters and the contribution of noises to the speckle contrast. In LSCI theory, tc is commonly assumed to be inversely proportional to the flow. The acquisition of the speckle data at multiple exposures and the subsequent non-linear fit on a pixel-wise basis are instrumentally complex and time-intensive tasks that prevent real-time computation of the flow maps. In the study, we evaluated the feasibility of machine learning analysis of MESI data to bypass the non-linear fitting procedure based on the synthetic exposure acquisition. Synthetic exposures limit acquisition bias due to imperfect illumination normalization and are less sensitive to camera noises except for low illumination conditions or imaging of fast flows. A residual convolutional neural network was adopted to predict the blood flow map based on a database of representative speckle images of channels in a microfluidic chip with calibrated flows. The MESI database contains images with different exposure times for different flow and different channel diameters. The database was spitted into a training and testing data set with a 50:50 ratio. Preliminary results showed that blood flow mapping using deep learning can achieve moderate accuracy and yield a more stable prediction with high noise-resistant ability, compared to pixel-wise non-linear fit.
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