Machine learning, in particular deep learning, has revolutionised fields as diverse as image recognition and text translation over the past decade, replacing pre-determined, 'hand-crafted' algorithms with flexible neural networks which learn to perform a task from training on existing examples. In recent years, the application of deep neural networks (DNNs) in the life and physical sciences has also attracted a lot of interest . Tomographic image reconstruction has witnessed a number of high-profile breakthroughs where DNNs match or even exceed the performance of state-of-the-art physics-based approaches . The majority of work applying DNNs for tomographic image reconstruction has focussed on enhancing the quality of real space images, which have been generated from sinograms by traditional algorithms, such as the filtered back projection . A number of notable exceptions do exist, where supervised learning, and generative models have been used to automatically map from sinogram to real space . While these methods are very promising, bottlenecks still exist to their application to image reconstruction due to their scalability (i.e. their ability to handle large images), their network size (large networks can be computationally very expensive) and particularly for applications where absolute values (as opposed to normalised values) are important in the reconstructed image, such as in chemical tomography and in quantitative analysis of attenuation-based tomography data.