Exploring the role of sample thickness for hyperspectral microscopy tissue discrimination through Monte Carlo simulations

Abstract

Recent advancements in multispectral (MS) and hyperspectral (HS) microscopy have focused on sensor and system improvements, yet sample processing remains overlooked. We conducted an analysis of the literature, revealing that 40% of studies do not report sample thickness. Among those that did report it, the vast majority, 98%, used 2–10 µm samples. This study investigates the impact of unstained sample thickness on MS/HS image quality through light transport simulations. Monte Carlo simulations were conducted on various tissue types (i.e., breast, colorectal, liver, and lung) using optical property parameters extracted from the literature. The simulations revealed that thin samples reduce tissue differentiation, while higher thicknesses (approximately 500 µm) improve discrimination, though at the cost of reduced light intensity. Although the results are based on idealized conditions and exclude certain real-world factors such as sample variability and instrument-specific effects, they highlight the need to study and optimize sample thickness for enhanced tissue characterization and diagnostic accuracy in MS/HS microscopy.