LAUSR

Cerenkov luminescence tomography (CLT) has been proved as an effective tool for various biomedical applications. Because of the severe scattering of Cerenkov luminescence, the performance of CLT remains unsatisfied. This paper proposed a novel CLT reconstruction approach based on a multilayer fully connected neural network (MFCNN). Monte Carlo simulation data was employed to train the MFCNN, and the complex relationship between the surface signals and the true sources was effectively learned by the network. Both simulation and in vivo experiments were performed to validate the performance of MFCNN CLT, and it was further compared with the typical radiative transfer equation (RTE) based method. The experimental data showed the superiority of MFCNN CLT in terms of accuracy and stability. This promising approach for CLT is expected to improve the performance of optical tomography, and to promote the exploration of machine learning in biomedical applications.