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

Area of Science:

• Biomedical Optics
• Spectroscopy
• Medical Imaging

Background:

• Spatially Offset Raman Spectroscopy (SORS) enables non-invasive molecular analysis of subsurface tissues, crucial for biomedical diagnostics.
• Clinical adoption of SORS is hindered by the challenge of maximizing Raman signal strength while adhering to laser safety regulations.

Purpose of the Study:

• To develop an integrated, open-source Python framework for optimizing SORS probe design.
• To quantitatively assess the photothermal safety and performance trade-offs of different SORS configurations.

Main Methods:

• Coupled Monte Carlo photon transport simulations with Pennes' bioheat and Arrhenius/CEM43 thermal damage models.
• Evaluated four SORS configurations (puck-point, ring-collector, iSORS, riSORS) on a multi-layer skin phantom.
• Integrated probe geometry optimization and photothermal safety modeling into a unified workflow.

Main Results:

• Ring-based illumination significantly reduced thermal loading, allowing 1-2 orders of magnitude longer safe exposure times compared to point illumination.
• The reinforced inverse SORS (riSORS) configuration demonstrated the optimal balance between subsurface selectivity and photon collection efficiency.
• The developed framework showed robustness to variations in optical properties, indicating patient variability resilience.

Conclusions:

• The study provides a quantitative link between SORS probe design, safety, and performance, crucial for clinical translation.
• The developed framework offers a practical roadmap for designing thermally safe and effective SORS systems for biomedical applications.
• Further experimental validation is necessary, but the simulation-based approach aids in designing clinically viable SORS devices.