Optimizing spatially offset Raman spectroscopy designs: balancing signal and safety in biomedical applications

Spatially offset Raman spectroscopy (SORS) offers non-invasive, molecularly specific access to subsurface tissues, showing strong potential for biomedical diagnostics. However, clinical translation remains limited by the need to balance Raman signal strength with laser safety constraints. This study introduces an open-source, Python-based framework integrating photon transport simulation, probe geometry optimization and photothermal safety modelling within a unified workflow. Monte Carlo photon transport is coupled with Pennes' bioheat and Arrhenius/CEM43 thermal damage models to assess four SORS configurations-conventional puck-point, ring-collector, inverse SORS (iSORS) and a new reinforced iSORS (riSORS)-on a multi-layer skin model. Results show that ring-based illumination markedly reduces thermal loading, extending safe laser exposure times by one to two orders of magnitude relative to point illumination, thus permitting up to 60-100× greater Raman energy accumulation before predicted damage onset. Among tested geometries, riSORS achieved the best trade-off between subsurface selectivity and photon collection efficiency, outperforming conventional designs in both signal yield and safety margin. Sensitivity analyses across optical properties further demonstrate robustness to patient variability. Although simplified assumptions require experimental validation, this framework quantitatively links probe design to safety-limited performance, offering a practical roadmap for clinically viable, thermally safe SORS system design.