  Deep learning based solutions are being succesfully implemented for a wide
variety of applications. Most notably, clinical use-cases have gained an
increased interest and have been the main driver behind some of the
cutting-edge data-driven algorithms proposed in the last years. For
applications like sparse-view tomographic reconstructions, where the amount of
measurement data is small in order to keep acquisition time short and radiation
dose low, reduction of the streaking artifacts has prompted the development of
data-driven denoising algorithms with the main goal of obtaining diagnostically
viable images with only a subset of a full-scan data. We propose WNet, a
data-driven dual-domain denoising model which contains a trainable
reconstruction layer for sparse-view artifact denoising. Two encoder-decoder
networks perform denoising in both sinogram- and reconstruction-domain
simultaneously, while a third layer implementing the Filtered Backprojection
algorithm is sandwiched between the first two and takes care of the
reconstruction operation. We investigate the performance of the network on
sparse-view chest CT scans, and we highlight the added benefit of having a
trainable reconstruction layer over the more conventional fixed ones. We train
and test our network on two clinically relevant datasets and we compare the
obtained results with three different types of sparse-view CT denoising and
reconstruction algorithms.
