• Scalability: Two new approaches in the architecture which radically reduce the number of parameters ○ Single digit input ○ Upsampling-type architecture after the last dense layer -this allows for decreasing the number of neurons in the last dense layer by a factor of at least 4 • Ability to suppress angular undersampling artefacts which we demonstrated using both simulated and experimental data • Information regarding absolute intensities is maintained; the images are not normalised • Ease-of-use: The code can be run by a non-expert and does not require multiple hyperparameter tuning in contrast to other conventional methods (e.g. SART/SIRT/CGLS as well as regularisation-based methods) • Simplicity: The addition of a discriminator network makes the training more complex and does not necessarily improve the resulting images (Figures S13-15 and Table )