Fouier Convolution Nerual Network for MRI reconstruction

Various image reconstruction methods have been proposed to reduce Magnetic resonance (MR) image acquisition time. One of recent trends is convolution neuron network (CNN) based deep learning model. However, most of these CNN models keep architecture of stacking small filters (e.g. 1×1 or 3×3) and the effective receptive field of these networks is limited, which is undesired for reconstruction because the random undersampling pattern causes global artifact. We proposed Fourier convolution block (FCB) to replace regular convolution filters. FCB can achieve both global receptive field and high computing efficiency by multiplication in frequency domain.