Optimal core size of fiber optic Raman probes for sub-surface tumor depth prediction: in-silico investigations

Abstract

Raman spectroscopy is evolving as an indispensable tool in cancer diagnosis owing to its label-free molecular probing ability. However, subsurface tumor assessment is still challenging using Raman spectroscopy. Researchers have adopted Spatially Offset Raman Spectroscopy to facilitate subsurface analysis. Recent works have demonstrated simultaneous subsurface tumor depth and thickness prediction using in-silico Spatially Offset Raman Spectroscopy investigations. These investigations are aided by fiber optic probes providing a good signal collection interface between sample and spectrometer, remote accessibility, precise control over illumination area, and Source Detector Separation. Optimizing the optical fiber probe parameters for photon collection will improve the outcome of tumor assessment. In this work, we analyze the importance of fiber optic collection core radius in subsurface tumor depth prediction using Monte Carlo simulated Spatially Offset Raman Spectroscopy signals. Simulations are carried out for varying illumination and collection fiber core radii, depths, and tumor thicknesses. An optimal collection fiber core radius for sub-surface depth prediction is estimated with a regression model for a given illumination fiber core radius. This optimization would improve the subsurface tumor localization capabilities of Raman Spectroscopy