Multi-attention prior based residual encoder-decoder network for fast and accurate reconstruction in fluorescence molecular tomography

Fluorescence molecular tomography (FMT) is a powerful modality for resolving the three-dimensional (3D) distribution of fluorescent targets inside biological tissues. However, the inverse problem of the FMT is severely ill-posed due to the strong scattering effects of photons inside biological tissues. Previously, regularization-based methods have been widely used to mitigate the ill-posedness of FMT. Due to the complex iterative computation and time-consuming reconstruction process, the FMT remains an intractable challenge for achieving accurate and fast 3D reconstructions. In this work, we propose a multi-attention prior based residual encoder-decoder network (MAP-REDN) to perform FMT reconstruction. Firstly, the multi-attention mechanism can provide weighted a priori information to the fluorescence source, enabling MAP-REDN to effectively mitigate the ill-posedness and enhance the reconstruction accuracy. Secondly, since the direct reconstruction strategy is adopted, the complex iterative computation process in the traditional regularization-based algorithms can be avoided, thus tremendously accelerating the reconstruction process. The experimental results demonstrate the feasibility of the MAP-REDN in achieving accurate and fast FMT reconstruction.