Magnetic resonance imaging method and device

Abstract

The invention discloses a magnetic resonance imaging method and device. According to the method, after a first deep neural network is used for reconstructing a first image, a first part of k-spatial data and a second part of k-spatial data extracted from complete k-spatial data are subjected to subtraction operation to obtain residual k-spatial data; then the residual k-spatial data is subjected to image reconstruction to obtain a residual image; and finally, the first image and the residual image are added to obtain a magnetic resonance image. In this way, the first part of the really collected k-spatial data is utilized in the processing carried out after the first image is reconstructed, the processing process of data fidelity terms is carried out outside the DNN reconstruction, and theDNN does not need to consider the processing of the data fidelity terms, so that the complexity and network parameters are lowered and reduced, and the image reconstruction speed is high. In addition, the processing process of the data fidelity terms is completed through sparse constraint reestablishing, without multi-time iteration, and the reconstruction speed can be improved on the premise that the image reconstruction stability is improved.

Description

technical field [0001] The present application relates to the technical field of medical imaging, in particular to a magnetic resonance imaging method and device. Background technique [0002] Magnetic Resonance Imaging (MRI) has high soft tissue contrast and spatial resolution, can simultaneously obtain the morphological information and functional information of the inspection site, and can flexibly select imaging parameters and imaging layers according to needs, and has become the current An important means of medical imaging examination. [0003] However, restricted by the Nyquist sampling theorem and the strength of the main magnetic field, the imaging speed of magnetic resonance is very slow, which greatly limits the clinical application of magnetic resonance. [0004] In order to speed up the speed of magnetic resonance imaging, Deep Neural Networks (DNN for short) has recently been applied in the field of magnetic resonance accelerated imaging. [0005] In the proce...

AI Technical Summary

Problems solved by technology

However, when the acquisition acceleration factor is high and the collected k-space data is small, this method needs multiple iterations to converge, resulting in slow reconstruction speed or poor reconstruction image quality

Images