Presentation + Paper
5 March 2021 Real-time oxygenation mapping from structured light imaging with deep learning
Author Affiliations +
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
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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KEYWORDS
Structured light

Tissues

Associative arrays

Model-based design

Performance modeling

Optical networks

Pathology

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