Advancing Multi-Photon Microscopy for Deep Tissue Imaging Through Monte Carlo Simulation Insights

Abstract

This computational study explores advancements in multi-photon microscopy for deep tissue imaging, with a primary focus on optimizing laser input parameters to enhance imaging depth, signal quality, and thermal management through Monte Carlo simulations. We utilized an already published multi- layer Monte Carlo model and validated, providing critical insights into light transport, fluorescence generation, and heat dissipation within biological tissues. The research emphasizes the superior performance of three-photon excitation microscopy compared to two-photon techniques in deeper imaging contexts, demonstrating enhanced signal-to-background ratios, minimized photodamage, and improved imaging fidelity. Comprehensive simulations were conducted to generate heat maps for various wavelengths and surface power levels across different depths, offering a detailed analysis of thermal dynamics for both two- and three-photon microscopy. Furthermore, AI-driven approaches for real-time optimization of imaging parameters are proposed, laying the foundation for the next generation of intelligent microscopy systems capable of delivering high-resolution, non-invasive imaging across complex biological structures. Sharif Hamza ALL RIGHTS RESERVED MS/MD 20201170