Deep Learning-based Human MRI Reconstruction and Preprocessing with Artificial Fourier Transform Network (AFT-Net)

Motivation: Complex-valued deep learning framework has not been fully investigated in human normal-field and low-field MRI reconstruction and preprocessing. Goal(s): We aim to replace conventional numerical methods with deep learning network, which reconstruct and preprocess the k-space data in parallel. Approach: An artificial Fourier transform network (AFT-Net) is proposed to directly processes the complex-valued raw data in the sensor domain. Results: An evaluation of accelerated reconstruction and denoised reconstruction shows that AFT-Net demonstrated the ability to reconstruct the data with significantly accelerate acquisition and random Gaussian noise. The proposed AFT-Net is an efficient and accurate approach for MRI reconstruction and preprocessing from raw data. Impact: MRI reconstruction and preprocessing with AFT-Net should be able to determine the domain-manifold mapping and process k-space data directly, which shows superior performance and can be served as an efficient and accurate approach for human high-field and low-field MRI acquisition.