Objective. Fluorescence molecular tomography (FMT), as an encouraging and non-invasive optical molecular imaging technology with strong specificity and sensitivity, has great potential for preclinical and clinical studies in tumor diagnosis, drug development, and therapeutic evaluation. However, the strong scattering of photons and the insufficient surface measurements make it very challenging to improve the quality of FMT reconstruction and practical application for early tumor detection. Therefore, continuous efforts have been made to explore more effective approaches or solutions in the pursuit of obtaining high-quality FMT reconstructions. Approach. This review takes a comprehensive overview of imaging methodology advances of FMT, mainly focusing on two critical issues in FMT reconstructions: improving the accuracy of solving the forward physical model and mitigating the ill-posed nature of the inverse problem from a methodological point of view. More importantly, numerous impressive and practical strategies and methods for improving the FMT reconstruction quality are summarized. Notably, the deep learning methods have been elaborately discussed to illustrate the advantages in promoting the imaging performance of FMT owing to the practicality of large datasets, the emergence of optimized algorithms, and the applications of innovative networks. Main results. The results demonstrate that the imaging quality of FMT can be effectively promoted by improving the accuracy of optical parameter modeling, combining with prior knowledge, and reducing dimensionality. In addition, the traditional regularization-based methods and the deep neural network-based methods, especially the end-to-end deep network, can enormously alleviate the ill-posedness of the inverse problem and improve the quality of FMT image reconstruction. Significance. This review aims at illustrating a variety of effective and practical methods for FMT image reconstruction, from which future research may benefit. Furthermore, it may provide some valuable research ideas and directions for FMT in the future, and could promote the development of FMT and other optical tomography.
