  This paper presents a new method for reconstructing regions of interest (ROI)
from a limited number of computed tomography (CT) measurements. Classical
model-based iterative reconstruction methods lead to images with predictable
features. Still, they often suffer from tedious parameterization and slow
convergence. On the contrary, deep learning methods are fast, and they can
reach high reconstruction quality by leveraging information from large
datasets, but they lack interpretability. At the crossroads of both methods,
deep unfolding networks have been recently proposed. Their design includes the
physics of the imaging system and the steps of an iterative optimization
algorithm. Motivated by the success of these networks for various applications,
we introduce an unfolding neural network called U-RDBFB designed for ROI CT
reconstruction from limited data. Few-view truncated data are effectively
handled thanks to a robust non-convex data fidelity term combined with a
sparsity-inducing regularization function. We unfold the Dual Block coordinate
Forward-Backward (DBFB) algorithm, embedded in an iterative reweighted scheme,
allowing the learning of key parameters in a supervised manner. Our experiments
show an improvement over several state-of-the-art methods, including a
model-based iterative scheme, a multi-scale deep learning architecture, and
other deep unfolding methods.
