Simulation and quantitative analysis of fluorescence intensity distribution based on the Monte Carlo method

Abstract

The Monte Carlo method is a versatile simulation algorithm to model the propagation of photons inside the biological tissues. It has been applied to the reconstruction of the fluorescence molecular tomography (FMT). However, such method suffers from low computational efficiency, and the time consumption is not desirable. One way to solve this problem is to introduce a priori knowledge which will facilitate iterative convergence. We presented an in vivo simulation environment for fluorescence molecular tomography (ISEFMT) using the Monte Carlo method to simulate the photon distribution of fluorescent objects and their sectional view in any direction quantitatively. A series of simulation experiments were carried out on different phantoms each with two fluorescent volumes to investigate the relationship among fluorescence intensity distribution and the excitation photon number, the locations and sizes of the fluorescence volumes, and the anisotropy coefficient. A significant principle was discovered, that along the direction of the excitation light, the fluorescent volume near the excitation point will provide shelter effect so that the energy of the fluorescent volume farther away from the excitation point is relatively lower. Through quantitative analysis, it was discovered that both the photon energy distribution on every cross section and the fluorescence intensity distributed in the two volumes exhibit exponential relationships. The two maximum positions in this distribution correspond to the centers of fluorescent volumes. Finally, we also explored the effect of the phantom coefficients on the exponential rule, and found out that the exponential rule still exists, only the coefficient of the exponential rule changed. Such results can be utilized in locating the positions of multiple fluorescent volumes in complicated FMT reconstruction involving multiple fluorescent volumes. Thus, a priori knowledge can be generalized, which would accelerate the reconstruction of FMT and even other images.