GAICN: Graph Attention Iterative Contraction Network for Bioluminescence Tomography

Abstract

Bioluminescence tomography (BLT) can provide non-invasive quantitative three-dimensional tumor information which has been widely applied in pre-clinical studies. Meanwhile, in recent years, deep learning methods have significantly improved the reconstruction resolution and speed by establishing a non-linear mapping relationship between surface-measured bioluminescence and light source distribution. However, this mapping relationship only works for specific biological tissues and light transmission processes under fixed wavelengths, resulting in poor stability and generalizability. To meet the requirements of diverse practical scenarios and inspired by more effective sparse regularization and graph representation theory, we propose a novel Graph Attention Iterative Contraction Network (GAICN) to conduct a finite element mesh spatial representation study. In the GAICN framework, two learnable spatial topological transforms based on the graph attention mechanism and an iterative contraction activation function were devised to achieve non-local feature aggregation and dynamic adjustment of weights between first-order neighboring nodes in the mesh. As a deep unrolling method, GAICN naturally inherits the coherence of surface bioluminescence with the light source in Forward-Backward Splitting (FBS), thus enhancing the generalizability, stability and interpretability of the network. Both simulation and in-vivo experiments further indicated that GAICN achieved superior reconstruction performance in terms of spatial location, dual light source resolution, stability, generalizability, as well as in-vivo practicability.