KEYWORDS: Data modeling, Near infrared spectroscopy, Brain tissue, Absorption, Education and training, Deep learning, Anatomy, Optical properties, Head, Brain
In this study, we present a physics-informed deep learning model for predicting partial pathlength and absorption changes in human brain using Near-Infrared Spectroscopy (NIRS). Leveraging the multi-layer modified Beer Lamber Law, our model overcomes the limitations of conventional approach that assumes tissue homogeneity. Trained on synthetic data generated from a multi-layer forward model, out model was tested on Monte Carlo simulations of both two and three-layer geometries, demonstrating robust performance despite encountering varying optical properties and anatomical complexities. Future work will focus on refining the model and testing it on multi-layer optical phantoms and human subjects.
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