The subject matter discussed herein relates to a fast magnetic resonance imaging (MRI) method to suppress fine-line artifact in Fast-Spin-Echo (FSE) images reconstructed with a deep-learning network. The network is trained using fully sampled NEX=2 (Number of Excitations equals to 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 network generated image in the loss function. However, only one of the excitations is retrospectively undersampled and inputted into 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.