The majority of popular existing algorithms for tomographic image reconstruction can be divided into two classes; direct methods and iterative methods. Direct methods such as filtered back projection (FBP) provide quick results which are artefact-free if there is an abundance of projections and data with high signal-to-noise ratio; iterative methods such as total-variation minimisation work well in sparse projection scenarios and/or data with low signal-to-noise ratio, but rely on prior knowledge and fine hyperparameter tuning . Recently DNNs have emerged as a powerful new tool for image reconstruction. DNNs have been applied to learn the filters for FBP or map the FBP to a DNN , to improve the quality of input projections/sinograms or of output images from direct reconstruction techniques .