K-space fluorescence tomography in reflectance: a comparison between deep learning and iterative solvers

Diffuse optical tomography, including fluorescence molecular tomography (FMT) have been greatly facilitated by the implementation of structured illumination (SI) strategies in recent years. In this work, we investigate the inverse problem in k-space reflectance fluorescence tomography. This in silico investigation leverages MCX, a Monte Carlo based platform, to generate large data sets for comparison between dAUTOMAP, a deep learning architecture, and commonly employed iterative solvers. We show that the proposed dAUTOMAP-based technique outperforms the traditional reconstruction algorithms. This new image formation approach is expected to facilitate imaging of sub-cutaneous tumors in live animals with enhanced resolution compared to the current gold standard.