  Filtered back projection (FBP) is a classical method for image reconstruction
from sinogram CT data. FBP is computationally efficient but produces lower
quality reconstructions than more sophisticated iterative methods, particularly
when the number of views is lower than the number required by the Nyquist rate.
In this paper, we use a deep convolutional neural network (CNN) to produce
high-quality reconstructions directly from sinogram data. A primary novelty of
our approach is that we first back project each view separately to form a stack
of back projections and then feed this stack as input into the convolutional
neural network. These single-view back projections convert the encoding of
sinogram data into the appropriate spatial location, which can then be
leveraged by the spatial invariance of the CNN to learn the reconstruction
effectively. We demonstrate the benefit of our CNN based back projection on
simulated sparse-view CT data over classical FBP.
