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

Ivan Ezhov; Kevin Scibilia; Luca Giannoni; Florian Kofler; Ivan Iliash; Felix Hsieh; Suprosanna Shit; Charly Caredda; Frédéric Lange; Bruno Montcel; Ilias Tachtsidis; Daniel Rueckert

doi:10.1117/1.JBO.29.9.093509

24 September 2024 Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue

Ivan Ezhov, Kevin Scibilia, Luca Giannoni, Florian Kofler, Ivan Iliash, Felix Hsieh, Suprosanna Shit, Charly Caredda, Frédéric Lange, Bruno Montcel, Ilias Tachtsidis, Daniel Rueckert

Author Affiliations +

Journal of Biomedical Optics, Vol. 29, Issue 9, 093509 (September 2024). https://doi.org/10.1117/1.JBO.29.9.093509

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.

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.

CC BY: © 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.

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Ivan Ezhov, Kevin Scibilia, Luca Giannoni, Florian Kofler, Ivan Iliash, Felix Hsieh, Suprosanna Shit, Charly Caredda, Frédéric Lange, Bruno Montcel, Ilias Tachtsidis, and Daniel Rueckert "Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue," Journal of Biomedical Optics 29(9), 093509 (24 September 2024). https://doi.org/10.1117/1.JBO.29.9.093509

Received: 29 May 2024; Accepted: 5 September 2024; Published: 24 September 2024

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KEYWORDS

Scattering

Data modeling

Education and training

Machine learning

Body composition

RGB color model

Near infrared spectroscopy

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