Separating structures of different fluorophore concentrations by principal component analysis on multispectral excitation-resolved fluorescence tomography images.

Multispectral excitation-resolved fluorescence tomography (MEFT) uses excitation light of different wavelengths to illuminate the fluorophores and obtains the reconstruction image frame which is fluorescence yield at each corresponding wavelength. For structures containing fluorophores of different concentrations, fluorescence yields show different variation trends with the excitation spectrum. In this study, principal component analysis (PCA) is used to analyze the MEFT reconstructed image frames. By taking advantage of the different variation trends of fluorescence yields, PCA can provide a set of principal components (PCs) in which structures containing different concentrations of fluorophores are shown separately. Simulations and experiments are both performed to test the performance of the proposed algorithm. The results suggest that the location and structure of fluorophores with different concentrations can be obtained and the contrast of fluorophores can be improved further by using this algorithm.