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 percent of studies do not report sample thickness. Among those that did report it, the vast majority, 98 percent, used 2 to 10 micrometer 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). The simulations revealed that thin samples reduce tissue differentiation, while higher thicknesses (approximately 500 micrometers) improve discrimination, though at the cost of reduced light intensity. These findings highlight the need to study and optimize sample thickness for enhanced tissue characterization and diagnostic accuracy in MS/HS microscopy.