Method for reconstructing sparse MRI (Magnetic resonance imaging) based on convolutional neural network in combination with iterative method

Abstract

The invention discloses a method for reconstructing sparse MRI (Magnetic resonance imaging) based on a convolutional neural network in combination with an iterative method. The method comprises the steps of firstly, preparing a data set which includes training data and test data, wherein the training data are used for training the network, and the test data are used for testing the trained data, each group of data includes a group of samples and labels, the samples are low-quality high-frequency images and low-frequency images with noise and image artifacts obtained by respectively zero-filling and reconstructing low-frequency data and high-frequency data which are acquired by dividing height down-sampling k-space data, and the labels are high-quality MR images without noise or image artifacts corresponding to the low-quality images; and training two networks with identical structures by use of the low-frequency data and the high-frequency data, respectively, wherein one network is used for reconstructing high-frequency k-space data, the other network is used for reconstructing low-frequency k-space data, and the addition of two reconstruction results is a final needed reconstruction result. According to the method for reconstructing the sparse MRI based on the convolutional neural network in combination with the iterative method, less k-space data are utilized, the reconstruction speed is faster and the image quality is higher.

Description

technical field [0001] The present invention relates to image processing, in particular to a sparse MRI reconstruction method based on the combination of convolutional neural network and iterative method. Background technique [0002] Magnetic resonance imaging (Magnetic Resonance Imaging, MRI) is realized by the action of high-frequency magnetic field outside the body, which generates signals from the material in the body radiating energy to the surrounding environment. The imaging process is similar to image reconstruction and CT. Compared with CT, its main advantages are: Ionizing radiation has no radioactive or biological damage to brain tissue. It can directly make tomographic images of cross-section, sagittal plane, coronal plane and various oblique planes, without artifacts such as ray hardening in CT images. It shows that the pathological process of the disease is more extensive and the structure is clearer than that of CT. Isodense lesions that are completely norm...

AI Technical Summary

Problems solved by technology

[0003] Purpose of the invention: The present invention aims at the problem of slow imaging in the prior art, and provides a sparse MRI reconstruction method based on the combination of convolutional neural network and iterative method. With the support of compressed sensing theory, the present invention combines deep learning and proposes In order to use the convolutional neural network to learn the sparse representation of the image, (the sparse representation of the image is the key point of the compressed sensing theory for MRI reconstruction of downsampling, the higher the sparseness of the image, the better the noise and artifacts in the image can be The removal, the structure in the image can be better restored

Images