The present invention discloses a magnetic resonance reconstruction method based on deep learning and data consistency, involving the field of magnetic resonance reconstruction methods; comprising 1: collecting K-space data and integrating it into a network composed of convolutional neural networks and data consistency layers in sequence to complete network construction; 2: converting the undersampled data in the K-space data into a folded image as the input of the constructed network, and converting the fully sampled data thereof into a complete image as the tagged data of the constructed network, and using the tagged data as a target to train the network through back propagation to obtain the mapping relationship between the network input and output; 3: inputting the corresponding image of the test set into the trained network for forward propagation to obtain the output image to complete the magnetic resonance reconstruction. The present invention solves the problems that the existing magnetic resonance reconstruction method based on deep learning does not make full use of the collected data and can only process single channels, resulting in poor reconstruction performance and stability, and achieves the effect of implementing supervision, improving learning ability and thus improving reconstruction performance.
