Real-time oxygenation mapping from structured light imaging with deep learning

Mason T. Chen; Nicholas J. Durr

doi:10.1117/12.2578939

5 March 2021 Real-time oxygenation mapping from structured light imaging with deep learning

Mason T. Chen, Nicholas J. Durr

Proceedings Volume 11639, Optical Tomography and Spectroscopy of Tissue XIV; 116391D (2021) https://doi.org/10.1117/12.2578939
Event: SPIE BiOS, 2021, Online Only

Abstract

Tissue oxygenation (StO₂), which is the fraction of oxygenated hemoglobin in biological tissues, is an important biomarker that can reveal information about tissue viability and underlying pathologies. The continuous monitoring of StO₂ is also useful for surgical guidance and patient management. In recent years, Spatial Frequency Domain Imaging (SFDI) has emerged as an elegant solution for mapping wide-field StO₂. However, conventional SFDI requires capturing a sequence of images at different spatial frequencies and wavelengths, resulting in slow acquisition times and challenges with moving objects. Model-based single-snapshot techniques have shortened the acquisition time but introduce image artifacts and decrease accuracy. Here we propose a deep-learning technique for real-time StO₂ mapping from snapshot structured light images. We train content-aware generative adversarial networks (OxyGAN) on pairs of structured light input at 659nm and 851nm wavelengths and StO₂ ground truth predicted by conventional SFDI. We demonstrate that OxyGAN is not only capable of rapid data acquisition and processing but is also more accurate than a model-based benchmark. We also compare OxyGAN to a hybrid model that uses separate networks to estimate optical absorption at two wavelengths followed by Beer-Lambert fitting. The end-to-end OxyGAN approach shows better performance in terms of both speed and accuracy. We additionally demonstrate real-time OxyGAN by applying it to videos of in vivo tissues. OxyGAN has the potential to enable wide-field, real-time, and accurate tissue oxygenation measurements in many clinical applications.

Conference Presentation

Citation Download Citation

Mason T. Chen and Nicholas J. Durr "Real-time oxygenation mapping from structured light imaging with deep learning", Proc. SPIE 11639, Optical Tomography and Spectroscopy of Tissue XIV, 116391D (5 March 2021); https://doi.org/10.1117/12.2578939

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