A quasi-analytic solution for real-time multi-exposure speckle imaging of tissue perfusion

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Purpose.Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality.Method.Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate.Result.Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI.Conclusion.The study's findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases.

Quasi-analytic solution for real-time multi-exposure speckle imaging of tissue perfusion

Laser speckle contrast imaging (LSCI) is a widefield imaging technique that enables high spatiotemporal resolution measurement of blood flow. Laser coherence, optical aberrations, and static scattering effects restrict LSCI to relative and qualitative measurements. Multi-exposure speckle imaging (MESI) is a quantitative extension of LSCI that accounts for these factors but has been limited to post-acquisition analysis due to long data processing times. Here we propose and test a real-time quasi-analytic solution to fitting MESI data, using both simulated and real-world data from a mouse model of photothrombotic stroke. This rapid estimation of multi-exposure imaging (REMI) enables processing of full-frame MESI images at up to 8 Hz with negligible errors relative to time-intensive least-squares methods. REMI opens the door to real-time, quantitative measures of perfusion change using simple optical systems.