Fast analysis of multiple exposure speckle data to provide relative blood flow maps using convolutional neural networks

Chao-Yueh Yu; Marc Chammas; Hirac Gurden; Hsin-Hon Lin; Frederic Pain

doi:10.1117/12.3017151

18 June 2024 Fast analysis of multiple exposure speckle data to provide relative blood flow maps using convolutional neural networks

Chao-Yueh Yu, Marc Chammas, Hirac Gurden, Hsin-Hon Lin, Frederic Pain

Author Affiliations +

Proceedings Volume 13009, Clinical Biophotonics III; 1300907 (2024) https://doi.org/10.1117/12.3017151
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Laser Speckle Contrast Imaging is a well-established technique able to produce relative blood flow maps contactless and without using dyes. It relies on the statistical analysis of dynamic speckle images, observed when a coherent light is used to illuminate a medium that contains moving scatterers. The local speckle contrast is related to the movements of the scatterers. Multiple exposure speckle imaging (MESI) is a variant of the technique that takes advantage of multiple exposure data to retrieve more quantitative flow maps by accounting for the unwanted and superimposed contribution of static scatterers. Yet, in MESI, a model is adjusted pixelwise to the experimental data requiring long computation times and an a priori guess on the flow regimes. These issues hindered so far, the translation of MESI to clinical applications though some studies have already demonstrated its potential. Here we propose an alternative method based on Convolutional Neural Networks to analyze MESI data. The proposed CNN architecture has been trained and validated using experimental data acquired on calibrated microfluidics flow phantoms. Then, the trained network was applied to analyze MESI data acquired in vivo in mice brain. In addition to be model-bias-free, we have found that the CNN approach infers flow maps much faster than the classical pixelwise regression approach. This new approach is promising for the clinical translation of MESI.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chao-Yueh Yu, Marc Chammas, Hirac Gurden, Hsin-Hon Lin, and Frederic Pain "Fast analysis of multiple exposure speckle data to provide relative blood flow maps using convolutional neural networks", Proc. SPIE 13009, Clinical Biophotonics III, 1300907 (18 June 2024); https://doi.org/10.1117/12.3017151

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