  In this paper we consider the problem of image reconstruction in optoacoustic
tomography. In particular, we devise a deep neural architecture that can
explicitly take into account the band-frequency information contained in the
sinogram. This is accomplished by two means. First, we jointly use a linear
filtered back-projection method and a fully dense UNet for the generation of
the images corresponding to each one of the frequency bands considered in the
separation. Secondly, in order to train the model, we introduce a special loss
function consisting of three terms: (i) a separating frequency bands term; (ii)
a sinogram-based consistency term and (iii) a term that directly measures the
quality of image reconstruction and which takes advantage of the presence of
ground-truth images present in training dataset. Numerical experiments show
that the proposed model, which can be easily trainable by standard optimization
methods, presents an excellent generalization performance quantified by a
number of metrics commonly used in practice. Also, in the testing phase, our
solution has a comparable (in some cases lower) computational complexity, which
is a desirable feature for real-time implementation of optoacoustic imaging.
