A convolutional recurrent neural networks (CRNN) with Non-Cartesian fidelity for 2D real-time imaging was proposed. 3D stack-of-star GRE radial sequence with self-navigator was used to acquire the data. Multiple respiratory phases were extracted from the navigator and the sliding window method was used to get the training data. The Fidelity constraints the reconstruction image to be consistent to the undersampled non-Cartesian k-space data. Convolution and recurrence improve the quality of the reconstructed images by using temporal dimension information. The reconstruction speed is around 10 frames/second, which fulfills the requirement of real-time imaging.