eprintid: 10197584 rev_number: 7 eprint_status: archive userid: 699 dir: disk0/10/19/75/84 datestamp: 2024-09-27 15:59:44 lastmod: 2024-09-27 15:59:44 status_changed: 2024-09-27 15:59:44 type: article metadata_visibility: show sword_depositor: 699 creators_name: Ezhov, Ivan creators_name: Scibilia, Kevin creators_name: Giannoni, Luca creators_name: Kofler, Florian creators_name: Iliash, Ivan creators_name: Hsieh, Felix creators_name: Shit, Suprosanna creators_name: Caredda, Charly creators_name: Lange, Frédéric creators_name: Montcel, Bruno creators_name: Tachtsidis, Ilias creators_name: Rueckert, Daniel title: Learnable real-time inference of molecular composition from diffuse spectroscopy of brain tissue ispublished: pub divisions: UCL divisions: B04 divisions: F42 keywords: Broadband near-infrared spectroscopy; hyperspectral; machine learning; brain imaging; Beer–Lambert law; spectral unmixing note: © 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. 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. date: 2024-09-24 date_type: published publisher: SPIE-Intl Soc Optical Eng official_url: https://doi.org/10.1117/1.JBO.29.9.093509 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2321688 doi: 10.1117/1.jbo.29.9.093509 lyricists_name: Lange, Frederic lyricists_id: FLANG91 actors_name: Lange, Frederic actors_id: FLANG91 actors_role: owner full_text_status: public publication: Journal of Biomedical Optics volume: 29 number: 09 article_number: 093509 issn: 1083-3668 citation: Ezhov, Ivan; Scibilia, Kevin; Giannoni, Luca; Kofler, Florian; Iliash, Ivan; Hsieh, Felix; Shit, Suprosanna; ... Rueckert, Daniel; + view all <#> Ezhov, Ivan; Scibilia, Kevin; Giannoni, Luca; Kofler, Florian; Iliash, Ivan; Hsieh, Felix; Shit, Suprosanna; Caredda, Charly; Lange, Frédéric; Montcel, Bruno; Tachtsidis, Ilias; Rueckert, Daniel; - view fewer <#> (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 <https://doi.org/10.1117/1.jbo.29.9.093509>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10197584/1/JBO-240152SSRR_online.pdf