A MRI Reconstruction Method Based on Deep Learning and Data Consistency

Abstract

The invention discloses a magnetic resonance reconstruction method based on deep learning and data consistency, and relates to the field of magnetic resonance reconstruction methods. The method comprises the steps: 1, collecting K-space data and integrating the K-space data into a network, formed by the sequential stacking of a convolutional neural network and a data consistency layer, for completing the network construction; 2, converting the undersampled data in the K-space data into a roll pleat image as the input of the built network, converting the full-sampled data of the K-space data into a complete image as the tag data of the built network, and obtaining a mapping relation between the network input and output through a back propagation training network; 3, inputting the corresponding images of a test set into the trained network, and performing the forward propagation to obtain an output image to complete the magnetic resonance reconstruction. The method solves the problems ofpoor reconstruction performance and stability caused by a condition that a conventional magnetic resonance reconstruction method based on deep learning does not fully utilize the collected data and can only deal with a single channel, achieves implementation supervision, improves the learning ability, and improves reconstruction performances.

Description

technical field [0001] The invention relates to the field of magnetic resonance reconstruction methods, in particular to a magnetic resonance reconstruction method based on deep learning and data consistency. Background technique [0002] Magnetic resonance imaging is a technique that uses the nuclear magnetic resonance phenomenon of hydrogen protons for imaging. Nuclei containing a single number of protons in the human body, such as the ubiquitous hydrogen nucleus, have spin motions for the protons. The spin motion of charged atomic nuclei is physically similar to individual small magnets, and the directionality distribution of these small magnets is random in the absence of external conditions. When the human body is placed in an external magnetic field, these small magnets will rearrange according to the magnetic force lines of the external magnetic field, specifically in two directions parallel to or antiparallel to the external magnetic field magnetic force lines, and ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is: the present invention provides a magnetic resonance reconstruction method based on deep learning and data consistency, which solves the problem that the existing deep learning-based magnetic resonance reconstruction method does not make full use of the collected data and can only process a single channel. Failure to improve the learning ability of the network leads to poor reconstruction performance and poor stability

Images