A Dual Domain Network For Mri Reconstruction Using Gabor Loss

Fast magnetic resonance imaging (MRI) scan is usually achieved by undersampling in k-space, and reconstruction methods to image domain is indispensable. Conventional reconstruction methods rely on independent sensitivity maps of the receiver coils to synthesize the components of the spatial harmonics in k-space. In recent years, deep learning-based MRI algorithms have been providing more accurate reconstructed image than the traditional results. Nonetheless, there is room for improvement. In this study, we proposed a new deep learning-based reconstruction algorithm to use image and k-space domain data simultaneously. The Gabor filter was also defined to effectively incorporate two different domain data in learning stage. Experimental results using real MRI data show that the proposed method outperforms other deep learning-based algorithms for three metrics of nMSE, SSIM, and VIF.