  Diffuse Optical Tomography (DOT) is an emerging technology in medical imaging
which employs light in the NIR spectrum to estimate the distribution of optical
coefficients in biological tissues for diagnostic and monitoring purposes. DOT
reconstruction implies the solution of a severely ill-posed inverse problem,
for which regularization techniques are mandatory in order to achieve
reasonable results. Traditionally, regularization techniques put a variance
prior on the desired solution/gradient via regularization parameters, whose
choice requires a fine tuning, specific for each case. In this work we explore
deep learning techniques in a fully data-driven approach, able of
reconstructing the generating signal (optical absorption coefficient) in an
automated way. We base our approach on the so-called Learned Singular Value
Decomposition, which has been proposed for general inverse problems, and we
tailor it to the DOT application. We perform tests with increasing levels of
noise on the measure, and compare it with standard variational approaches.
