Keywords: Image Reconstruction, Machine Learning/Artificial IntelligenceConventional medical image reconstruction methods are less parametric and lack generality due to random error and noise. A novel artificial Fourier transform (AFT) framework is developed which determines the mapping between k-space and i-space like DFT while can be fine-tuned with further training. The flexibility of AFT allows it to be simply incorporated into any existing deep learning network as learnable or static blocks. Reconstruction and denoising tasks are combined into a unified network that simultaneously enhances the image quality. AFT-Net achieves competitive results compared with other methods and proofs to be more robust to additional noise and contrast differences.
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