Super-Resolution Magnetic Resonance Imaging using Segmented Signals in Phase-Scrambling Fourier Transform Imaging and Deep Learning

We propose an image acquisition and reconstruction method based on segment-wise signal sampling and skipping. Unrolling model-based deep learning reconstruction is used to improve the quality of reconstructed images and reduce the reconstruction time. Simulation experiments show that the skipped signals were reconstructed in the iterative reconstruction based on a convolutional neural network and that the evaluation scores of reconstructed images were improved for unsegmented band-limited signals. The proposed method is applied to an experimentally obtained phase-scrambling Fourier transform signal to demonstrate its effectiveness.