@article{discovery10197584,
          number = {09},
            year = {2024},
           month = {September},
         journal = {Journal of Biomedical Optics},
           title = {Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue},
          volume = {29},
            note = {{\copyright} The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.},
       publisher = {SPIE-Intl Soc Optical Eng},
            issn = {1083-3668},
        keywords = {Broadband near-infrared spectroscopy; hyperspectral; machine learning; brain imaging; Beer-Lambert law; spectral unmixing},
             url = {https://doi.org/10.1117/1.JBO.29.9.093509},
          author = {Ezhov, Ivan and Scibilia, Kevin and Giannoni, Luca and Kofler, Florian and Iliash, Ivan and Hsieh, Felix and Shit, Suprosanna and Caredda, Charly and Lange, Fr{\'e}d{\'e}ric and Montcel, Bruno and Tachtsidis, Ilias and Rueckert, Daniel},
        abstract = {SIGNIFICANCE: 
Diffuse optical modalities such as broadband near-infrared spectroscopy (bNIRS) and hyperspectral imaging (HSI) represent a promising alternative for low-cost, non-invasive, and fast monitoring of living tissue. Particularly, the possibility of extracting the molecular composition of the tissue from the optical spectra deems the spectroscopy techniques as a unique diagnostic tool.


AIMS: 
No established method exists to streamline the inference of the biochemical composition from the optical spectrum for real-time applications such as surgical monitoring. We analyze a machine learning technique for inference of changes in the molecular composition of brain tissue.


APPROACH: 
We propose modifications to the existing learnable methodology based on the Beer-Lambert law. We evaluate the method's applicability to linear and nonlinear formulations of this physical law. The approach is tested on data obtained from the bNIRS- and HSI-based monitoring of brain tissue.



RESULTS: 
The results demonstrate that the proposed method enables real-time molecular composition inference while maintaining the accuracy of traditional methods. Preliminary findings show that Beer-Lambert law-based spectral unmixing allows contrasting brain anatomy semantics such as the vessel tree and tumor area.


CONCLUSION: 
We present a data-driven technique for inferring molecular composition change from diffuse spectroscopy of brain tissue, potentially enabling intra-operative monitoring.}
}