Ezhov, Ivan;
Scibilia, Kevin;
Giannoni, Luca;
Kofler, Florian;
Iliash, Ivan;
Hsieh, Felix;
Shit, Suprosanna;
... Rueckert, Daniel; + view all
(2024)
Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue.
Journal of Biomedical Optics
, 29
(09)
, Article 093509. 10.1117/1.jbo.29.9.093509.
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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.
| Type: | Article |
|---|---|
| Title: | Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/1.jbo.29.9.093509 |
| Publisher version: | https://doi.org/10.1117/1.JBO.29.9.093509 |
| Language: | English |
| Additional information: | © 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. |
| Keywords: | Broadband near-infrared spectroscopy; hyperspectral; machine learning; brain imaging; Beer–Lambert law; spectral unmixing |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10197584 |
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