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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)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Beer–lambert Law ; Brain Imaging ; Broadband Near-infrared Spectroscopy ; Hyperspectral ; Machine Learning ; Spectral Unmixing; Robust; Neuronavigation; Images
ISSN (print) / ISBN 1083-3668
e-ISSN 1560-2281
Zeitschrift Journal of Biomedical Optics
Quellenangaben Band: 29, Heft: 9, Seiten: , Artikelnummer: 093509 Supplement: ,
Verlag SPIE
Verlagsort 1000 20th St, Po Box 10, Bellingham, Wa 98225 Usa
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Förderungen
European Union's Horizon Europe Research