A deep learning approach to enhance the temporal resolution of laser speckle imaging system for blood flow measurement

Sivakumar Panneer Selvam; Murali K.; Soumyajit Sarkar; Sibi Raj B. Pillai; Hari M. Varma

doi:10.1117/12.2686893

9 August 2023 A deep learning approach to enhance the temporal resolution of laser speckle imaging system for blood flow measurement

Sivakumar Panneer Selvam, Murali K., Soumyajit Sarkar, Sibi Raj B. Pillai, Hari M. Varma

Author Affiliations +

Proceedings Volume 12628, Diffuse Optical Spectroscopy and Imaging IX; 126282O (2023) https://doi.org/10.1117/12.2686893
Event: European Conferences on Biomedical Optics, 2023, Munich, Germany

Abstract

Laser speckle based superficial and deep tissue blood flow imaging is gaining interest with the advent of high speed cameras. Multi-exposure speckle intensity images are often utilized for this purpose, owing to the better quantification of flow. However, any uncertainty in selecting the required exposure range apriori and the data acquisition time associated with multi-exposure intensity measurements limit the temporal resolution of these systems. To address these concerns, we propose a deep learning-based imputation using Generative Adversarial Imputation Network (GAIN) to generate additional temporal samples from coarsely acquired multi-exposure speckle data. The feasibility of the proposed method has been verified by using simulations where the trade-off between temporal resolution and the accuracy of flow measurement is minimized.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Sivakumar Panneer Selvam, Murali K., Soumyajit Sarkar, Sibi Raj B. Pillai, and Hari M. Varma "A deep learning approach to enhance the temporal resolution of laser speckle imaging system for blood flow measurement", Proc. SPIE 12628, Diffuse Optical Spectroscopy and Imaging IX, 126282O (9 August 2023); https://doi.org/10.1117/12.2686893

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