In 2018 the AUTOMAP network demonstrated the application of direct reconstructions of images from projections , however the size of image for which AUTOMAP can be applied is limited by the presence of densely connected layers with many parameters, which scale poorly with the number of pixels in the input. Alternative methods based on convolutional neural networks (CNNs) and generative adversarial networks (GANs) that include physical information have recently been proposed which show great promise for image reconstruction, with fewer restrictions of the sizes of data that can be treated . The GAN approach has been demonstrated to be very useful for image reconstruction, but previous procedures rely on normalising sinogram and image values 39 . This is not necessarily a problem when the data analysis focuses on image segmentation, but in the case of chemical tomography where we are reconstructing images containing spectra at each pixel this information is essential ; the absolute values are also required when quantitative analysis of attenuation-based tomography data is performed (e.g. micro-CT).