The ability to accurately reconstruct images from sparsely-sampled sinograms is critical for timeresolved in situ/operando tomography experiments as well as for reducing X-ray dose in medical CT. In its current form, the neural network cannot be compared to FBP in terms of speed but we have demonstrated its potential to suppress angular undersampling using real experimental data. Furthermore, the network could be potentially applied to other tomographic methods and modalities, such as neutron tomography and X-ray fluorescence tomography. Last but not least, the network has been developed for tomographic image reconstruction using 2D parallel / pencil beam geometries but we can foresee its application for other inverse problems in imaging if the appropriate forward model is known, such as the parallax problem in XRD-CT.