Magnetic resonance imaging method, device and system and storage medium

Abstract

The invention discloses a magnetic resonance imaging method, device and system and a storage medium. The magnetic resonance imaging method comprises the steps of: acquiring an original model of magnetic resonance imaging, and establishing an initial imaging model according to an iterative algorithm used for solving the original model, wherein the iterative algorithm comprises at least one of an undetermined parameter, an undetermined solving operator and an undetermined structure relation; training the initial imaging model based on the sample data to generate a magnetic resonance imaging model, wherein the training of the initial imaging model is used for learning at least one of the undetermined parameter, the undetermined solving operator and the undetermined structural relationship inthe iterative algorithm; and acquiring to-be-processed undersampled K spatial data, and inputting the undersampled K spatial data into the magnetic resonance imaging model to generate a magnetic resonance image. Compared with a traditional mode, the magnetic resonance imaging method improves the degrees of freedom of the magnetic resonance imaging model, and the magnetic resonance imaging model obtained through learning can improve the quality of a magnetic resonance reconstructed image.

Description

technical field [0001] Embodiments of the present invention relate to deep learning technology, and in particular to a magnetic resonance imaging method, device, system, and storage medium. Background technique [0002] Magnetic resonance uses static magnetic field and radio frequency magnetic field to image human tissue. It not only provides rich tissue contrast, but also is harmless to human body, so it has become a powerful tool for medical clinical diagnosis. However, the slow imaging speed has always been a major bottleneck restricting its rapid development. [0003] In terms of fast imaging, the commonly used technologies are parallel imaging and compressed sensing. Parallel imaging uses the correlation between multi-channel coils to accelerate acquisition, while compressed sensing uses the prior information of the sparseness of the imaged object to reduce k-space sampling points. However, limited by hardware and other conditions, the acceleration of parallel imaging...

AI Technical Summary

Problems solved by technology

However, limited by hardware and other conditions, the acceleration of parallel imaging is limited, and with the increase of the acceleration, the image will appear noise amplification phenomenon; and compressed sensing technology uses iterative reconstruction to make the reconstruction time very long, and it is difficult to choose sparse transformation and Reconstruction parameters

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