TSR-Net: A Two-Step Reconstruction Approach for Cherenkov-Excited Luminescence Scanned Tomography

Cherenkov-excited luminescence scanned tomography (CELST) can recover a high-resolution 3D distribution of luminescent sources within tissue. However, reconstructing the distribution of the quantum field from boundary measurements is a typical ill-posed problem. In this work, we propose a novel two-step reconstruction network (TSR-Net) based on a fusion mechanism, that integrates two encoder-decoder networks (ED-Net) using a concatenation block. Firstly, an ED-Net is trained to learn the CT structural features of tissues with the measured data. Then, the trained ED-Net is fixed and cascaded by another ED-Net for a second-step training to predict the 3D distributions. Numerical simulations reveal that the proposed approach can not only accurately reconstruct the intensity values of the luminescent sources, but also achieve a reconstruction resolution of 1mm with low target-background contrast. Furthermore, the well-trained network is still effective in the reconstruction of tissues with different shapes, which indicates an excellent generalization ability of the algorithm.