MRI reconstruction based on geometry distillation and feature adaptation

Although the existing compressed sensing-magnetic resonance imaging (CS-MRI) methods based on deep learning have achieved good results, the interpretability of these methods still faces challenges, and the transition from theoretical analysis to network design is not natural enough. In order to solve the above problems, this paper proposed a deep dual-domain geometry distillation feature adaptive network (DDGD-FANet). The deep unfolding network iteratively expanded the MRI reconstruction optimization problem into three sub-modules: data consistency module, dual-domain geometry distillation module, and adaptive network module. It could compensate for the lost context information of the reconstructed image, restore more texture details, remove global artifacts, and further improve the reconstruction effect. Three different sampling modes were used in the public dataset. The results show that DDGD-FANet achieves a higher peak signal-to-noise ratio and structural similarity index in all three sampling modes. At the Cartesian 10% compressed sensing(CS )ratio, the peak signal-to-noise ratio is increased by 5.01 dB, 4.81 dB, and 3.34 dB, respectively, higher than that of iterative shrinkage-thresholding algorithm (ISTA)-Net +, fast ISTA (FISTA)-Net, and DGDN models.