Magnetic resonance reconstruction method based on deep learning and convex set projection

Abstract

The invention discloses a magnetic resonance reconstruction method based on deep learning and convex set projection, and relates to the technical field of magnetic resonance. The method comprises thesteps that S1, a network is constructed according to overlapping structures of multiple convolutional neural network modules and multiple convex set projection layers and shared data, wherein the shared data includes acquired K-space data and coil sensitivity information, and the convex set projection layers are obtained on the basis of the shared data; S2, after the network is constructed, all network parameters are trained through a reverse propagation process and verified; S3, structure and operation characteristics of the network are determined according to the verified network parameters,known test set data is input, the forward propagation of the network is conducted, unknown mapping data is obtained, and magnetic resonance reconstruction is completed. The method solves the problemthat by means of a current magnetic resonance reconstruction technology based on deep learning, only single-channel magnetic resonance data can be supported, and multi-channel magnetic resonance datacannot be processed.

Description

technical field [0001] The invention relates to the technical field of magnetic resonance, in particular to a magnetic resonance reconstruction method based on deep learning and convex set projection. 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 the abov...

AI Technical Summary

Problems solved by technology

However, the above approximate transformation is difficult to describe the complex micro-microbial structure, which leads to blurred or mosaic effect in the reconstructed image

Third, compressive sensing defines MRI reconstruction as a nonlinear optimization problem, thus resulting in long reconstruction times

However, this technology currently only supports single-channel MRI data and cannot handle multi-channel MRI data

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