Evaluation of a high framerate multi-exposure laser speckle contrast imaging setup

Abstract

We present a first evaluation of a new multi-exposure laser speckle contrast imaging (MELSCI) system for assessing spatial variations in the microcirculatory perfusion. The MELSCI system is based on a 1000 frames per second 1-megapixel camera connected to a field programmable gate arrays (FPGA) capable of producing MELSCI data in realtime. The imaging system is evaluated against a single point laser Doppler flowmetry (LDF) system during occlusionrelease provocations of the arm in five subjects. Perfusion is calculated from MELSCI data using current state-of-the-art inverse models. The analysis displayed a good agreement between measured and modeled data, with an average error below 6%. This strongly indicates that the applied model is capable of accurately describing the MELSCI data and that the acquired data is of high quality. Comparing readings from the occlusion-release provocation showed that the MELSCI perfusion was significantly correlated (R=0.83) to the single point LDF perfusion, clearly outperforming perfusion estimations based on a single exposure time. We conclude that the MELSCI system provides blood flow images of enhanced quality, taking us one step closer to a system that accurately can monitor dynamic changes in skin perfusion over a large area in real-time.

Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry

“…As expected, it was found that single exposure time LSCI performed relatively poorly compared to models that included multiple exposure times. The Kazmi model 12 that has been used in some studies for calculating perfusion or flow from multiexposure contrasts [20][21][22] performed better. However, it was inferior to the performance of ANN-MELSCI.…”