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.
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) from that of moving scatterers (red blood cells). First, we have evaluated how a synthetic exposure acquisition scheme could strongly simplify the instrument for MESI, while remaining quantitative over a range of relevant flows. A microfluidic chip with controlled flows in channels with dimension representative of mice brain cerebral vasculature has been imaged using the classical modulated intensities approach and the synthetic exposure mode. This study allowed to propose guidelines in terms of readout dark noise and spatial response uniformity for the choice of a camera for MESI in the synthetic exposure mode. Second, we have evaluated how unwanted movements introduce bias in the speckle contrast calculation for a representative range of movement speeds. Mixed solutions of intralipid and glycerin in Brownian motion have been characterized to provide calibrated samples in terms of scatterers de-correlation times. High concentration of glycerin led to decorrelation times of several ms corresponding to actual values in small capillaries while low concentration of glycerin led to decorrelation times of 1ms or less corresponding to arterioles and arteries. The effects of the unwanted movement speed and direction have been measured for both lateral (x-y) and axial (z) movements. The bias introduced by unwanted movement in the (x-y) plane depends on the relative values of the time between frames and the scatterers decorrelation. In addition, for axial movements, parameters such as the numerical aperture (NA) and the magnification level (M) need to be considered due to their role in defining the depth of field.
We have compared multiple exposure speckle imaging using two approaches: (i) duration modulation of laser diode pulses with fixed exposure time and (ii) synthetic exposure created from the sum of frames obtained at 1ms exposure time. Both methods have been applied to evaluate controlled flows in micro-channels. The results demonstrate that the synthetic exposure method provides accurate speckle contrast data over a wide range of exposures, channel diameters and flows.
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