Residual learning network for accurate and stable reconstruction in Cerenkov luminescence tomography

Cerenkov luminescence tomography (CLT) is a highly sensitive and promising imaging modality for three-dimensional visualization of radiopharmaceuticals. However, the approximate error generated by the simplified radiation transfer equation and the ill-posedness of the inverse problem limit the improvement of CLT reconstruction. In this research, a residual learning network (RLN) was proposed to improve morphological restorability. By learning the relationship between surface photon intensity and internal source, the errors from the inverse process could be avoided. RLN comprised two fully connected sub-networks: one was used to provide the coarse reconstruction result. The other optimized the final reconstruction result by learning the residual between the coarse reconstruction result and the true source. Monte Carlo method was used to generate the dataset. Furthermore, multilayer fully connected neural network (MFCNN) was used as baselines and compared. Single-source simulation and robustness experiments were conducted to evaluate the reconstruction performance. The experimental results show RLN achieved accurate localization and morphological reconstruction, which will promote the application of machine learning in optical tomography reconstruction.