CPC G01R 33/5608 (2013.01) [G01R 33/4818 (2013.01); G06T 7/0012 (2013.01); G06T 7/168 (2017.01); G06T 7/174 (2017.01); G06T 11/005 (2013.01); G06T 11/006 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20056 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20084 (2013.01)]  10 Claims 
1. A magnetic resonance imaging (MRI) undersampling and reconstruction method based on a crossdomain network, wherein the method comprises:
(1) acquiring and preprocessing head magnetic resonance (MR) images, and obtaining simulated fullsampled kspace data through Fourier Transform;
(2) separating real and imaginary parts of the simulated fullsampled kspace data obtained in step (1), and saving real and imaginary parts in two matrices with same dimensions independently, then merging the real and imaginary parts into two channels as input for the crossdomain network;
(3) constructing the crossdomain network, which includes an undersampling layer, an Inverse Fourier Transform layer, and a reconstruction network, by training the network with data obtained in step (2) as input, the undersampling layer simulating a process of kspace undersampling in real scenes, the Inverse Fourier Transform layer connecting the Fourier domain with an image domain and obtaining undersampled MR images via the Inverse Fourier Transform, the reconstruction network recovering details of the undersampled MR images to obtain a final image, after completing the training, and obtaining a trained crossdomain network;
(4) using the trained crossdomain network obtained in step (3) to undersample and reconstruct head MR images:
(41) setting different sampling rates of the undersampling layer in step (3), optimizing the trained crossdomain network to obtain probability matrices and corresponding reconstruction networks under the different sampling rates, generating optimal undersampling trajectories based on the probability matrices and regional sampling distance constraints;
(42) quantitatively analyzing the relationship between the probability matrices and the different sampling rates according to the probability matrices under the different sampling rates, obtaining functional expressions of a 3D probability curve P_{face}, a central probability curve P_{center }and a marginal probability curve P_{margin }by data fitting;
(43) based on the functional expressions of P_{face}, P_{center}, P_{margin}, and the regional sampling distance constraints, generating probability matrices and undersampling trajectories under the different sampling rates;
(44) undersampling kspace data based on the undersampling trajectories obtained in step (43) and generate undersampled MR images via the Inverse Fourier Transform layer, the reconstruction network reconstructing the undersampled MR images to recover the details.
