Fine-line artifact is suppressed in Fast Spin Echo (FSE) images reconstructed with a deep-learning network. The network is trained using many examples of fully sampled Nex=2 data. In each case the two excitations are combined to generate fully sampled ground-truth images with no fine-line artifact, which are used for comparison with the generated image in the loss function. However only one of the excitations is retrospectively undersampled and fed into the input of the network during training. In this way the network learns to remove both undersampling and fine-line artifacts. At inferencing, only Nex=1 undersampled data are acquired and reconstructed.