Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning

Significance: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverseMonte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC x mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances inmulti-exposure LSCI (MELSCI) enable the analysis in an imaging modality. Aim: To present a method for speed-resolved perfusion imaging in absolute units %RBC x mm/s, computed from multi-exposure speckle contrast images. Approach: An artificial neural network (ANN) was trained on a large simulated dataset of multi- exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. Results: Speed-resolved perfusion was estimated in the three speed intervals 0 to 1 mm/s, 1 to 10 mm/s, and > 10 mm/s, with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. Conclusions: The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique. (c) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Automated summary

Multi-exposure speckle contrast imaging (MELSCI) allows for the calculation of speed-resolved perfusion in absolute units, improving the physiological interpretation of data and increasing the clinical impact of the technique.