  Medical imaging is nowadays a pillar in diagnostics and therapeutic
follow-up. Current research tries to integrate established - but ionizing -
tomographic techniques with technologies offering reduced radiation exposure.
Diffuse Optical Tomography (DOT) uses non-ionizing light in the Near-Infrared
(NIR) window to reconstruct optical coefficients in living beings, providing
functional indications about the composition of the investigated organ/tissue.
Due to predominant light scattering at NIR wavelengths, DOT reconstruction is,
however, a severely ill-conditioned inverse problem. Conventional
reconstruction approaches show severe weaknesses when dealing also with mildly
complex cases and/or are computationally very intensive. In this work we
explore deep learning techniques for DOT inversion. Namely, we propose a fully
data-driven approach based on a modularity concept: first data and originating
signal are separately processed via autoencoders, then the corresponding
low-dimensional latent spaces are connected via a bridging network which acts
at the same time as a learned regularizer.
