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Ezhov, I.* ; Scibilia, K.* ; Giannoni, L.* ; Kofler, F. ; Iliash, I.* ; Hsieh, F.* ; Shit, S.* ; Caredda, C.* ; Lange, F.* ; Montcel, B.* ; Tachtsidis, I.* ; Rueckert, D.*

Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.

J. Biomed. Opt. 29:093509 (2024)

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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. AIM: 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.

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