Fluorescence spectroscopy is a technique proposed to improve the detection of tumor boundaries during fluorescence-guided neurosurgery. More sensitive than fluorescence microscopy, this technique can provide spectral measurements of fluorescence that are post-processed to extract qualitative or even quantitative information on fluorophores present in the tissue probed. To obtain quantitative information, this technique requires modeling the propagation of radiation in the brain, considering the optical properties of the tissue as well as the fluorescence phenomenon. The present work is devoted to the development and application of Monte Carlo methods including the fluorescence phenomenon, enabling us to study the effects of optical properties on the measured signal and, consequently, on biomarker quantification. Symbolic Monte Carlo method based on orthogonal polynomial sequences is developed to express a physical observable as a function of the fluorophore concentration in a single simulation. The results obtained for the case of a brain composed of grey matter and different fluorophore concentrations are studied and show good agreement with standard Monte Carlo approaches.
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Gabory et al. (2024) studied this question.
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