The invention relates to a neural network magnetic
resonance image
reconstruction method based on double-domain alternating
convolution. The neural network magnetic
resonance image
reconstruction method is characterized by comprising the following steps: the step 1, obtaining a
K space data set; the step 2, generating under-sampling
K space data; the step 3, establishing a coding and decoding neural
network structure of double-domain alternating
convolution; the step 4, training a coding and decoding neural
network model of double-domain alternating
convolution by using the under-sampled
K space data generated in the step 2 and
image domain data obtained by performing inverse
Fourier transform on the K domain information in the step 1; and the step 5, reconstructing the undersampled magnetic
resonance data by using the trained double-domain alternating coding and decoding neural network to obtain a magnetic resonance reconstructed image with relatively
high definition. According to the method, accelerated reconstruction of
magnetic resonance imaging is realized by using the small kernel
convolutional neural network on the K domain, artifacts caused by breaking through the Nyquist sampling limit are eliminated, meanwhile, clear
magnetic resonance imaging can be reconstructed, and the reconstruction precision is improved.