Abstract
.Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient for noise-free data, when a moderate degree of noise is present, and for in vivo data from an occlusion-release experiment.
Highlights
The microcirculatory blood flow can be assessed using laserbased techniques, which indirectly quantify the Doppler shifts that occur when light is scattered by moving red blood cells
laser Doppler flowmetry (LDF) perfusion and multiexposure LSCI (MELSCI) contrasts are calculated for the same tissue models, and an artificial neural network (ANN) is trained with contrasts as input and LDF perfusion as target
This study shows that by using a machine learning approach with artificial neural networks (ANN) to analyze multiexposure laser speckle contrast data, conventional laser Doppler perfusion can be resembled with high accuracy
Summary
The microcirculatory blood flow can be assessed using laserbased techniques, which indirectly quantify the Doppler shifts that occur when light is scattered by moving red blood cells. These techniques are commonly based on either a temporal[1,2] or spatial[3,4] analysis of the dynamic speckle pattern formed when Doppler shifted and nonshifted light is backscattered from a laser illuminated tissue and mixed on a detector. In laser Doppler flowmetry (LDF), an almost direct mapping of Doppler shifts can be attained by studying the frequency distribution of the temporal speckle fluctuations This technique can produce perfusion estimations that are proportional to the blood flow speed if the Nyquist criterion for the photodetector current is fulfilled. Compared to LDF, there is no direct relationship between the distribution
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