Compressed sensing allowed speeding up MRI image acquisition by several folds but with increased reconstruction time. Deep-learning was introduced for fast reconstruction with comparable or improved reconstruction quality. However, its applications on non-cartesian grids are scarce, mostly based on simulated resampling from original Cartesian grids. Here, we evaluated the performance of two deep-learning networks for reconstructing images acquired with k-space spiral trajectories in real MRI scanning. We demonstrated that deep learning networks can successfully reconstruct high-resolution images from under-sampled spiral trajectories with half of data in k-space and their performance is robust to spiral trajectories different from training.