High-fidelity Mesoscopic Fluorescence Molecular Tomography based on SSB-Net

The imaging fidelity of mesoscopic fluorescence molecular tomography (MFMT) in reflective geometry suffers from spatial non-uniformity of measurement sensitivity and ill-posed reconstruction. In this study, we present a spatially adaptive split Bregman network (SSB-Net) to simultaneously overcome the spatial nonuniformity of measurement sensitivity and promote reconstruction sparsity. SSB-Net is derived by unfolding the split Bregman algorithm. In each layer of SSB-Net, residual block and 3D convolution neural networks (3D-CNN) can adaptively learn the spatially non-uniform error compensation, spatially dependent proximal operator, and sparsity transformation. Simulations and experiments show that the proposed SSB-Net enables high-fidelity MFMT tomographic reconstruction of multifluorophores at different positions within a depth of a few millimeters. Our method paves the way for a practical reflection-mode diffuse optical imaging technique.