A cross-domain complex convolution neural network for undersampled magnetic resonance image reconstruction

To introduce a new cross -domain complex convolution neural network for accurate MR image reconstruction from undersampled k -space data. Most reconstruction methods utilize neural networks or cascade neural networks in either the image domain and/or the k -space domain. However, these methods encounter several challenges: 1) Applying neural networks directly in the k -space domain is suboptimal for feature extraction; 2) Classic image -domain networks have difficulty in fully extracting texture features; and 3) Existing cross -domain methods still face challenges in extracting and fusing features from both image and k -space domains simultaneously. In this work, we propose a novel deep -learning -based 2-D single -coil complex -valued MR reconstruction network termed TEID-Net. TEID-Net integrates three modules: 1) TE-Net, an image -domain -based sub -network designed to enhance contrast in input features by incorporating a Texture Enhancement Module; 2) ID -Net, an intermediate -domain sub -network tailored to operate in the image -Fourier space, with the specific goal of reducing aliasing artifacts realized by leveraging the superior incoherence property of the decoupled onedimensional signals; and 3) TEID-Net, a cross -domain reconstruction network in which ID -Nets and TE-Nets are combined and cascaded to boost the quality of image reconstruction further. Extensive experiments have been conducted on the fastMRI and Calgary-Campinas datasets. Results demonstrate the effectiveness of the proposed TEID-Net in mitigating undersampling-induced artifacts and producing high -quality image reconstructions, outperforming several state-of-the-art methods while utilizing fewer network parameters. The cross -domain TEID-Net excels in restoring tissue structures and intricate texture details. The results illustrate that TEID-Net is particularly well -suited for regular Cartesian undersampling scenarios.