Improving accelerated MRI by deep learning with sparsified complex data

Purpose To obtain high‐quality accelerated MR images with complex‐valued reconstruction from undersampled k‐space data. Methods The MRI scans from human subjects were retrospectively undersampled with a regular pattern using skipped phase encoding, leading to ghosts in zero‐filling reconstruction. A complex difference transform along the phase‐encoding direction was applied in image domain to yield sparsified complex‐valued edge maps. These sparse edge maps were used to train a complex‐valued U‐type convolutional neural network (SCU‐Net) for deghosting. A k‐space inverse filtering was performed on the predicted deghosted complex edge maps from SCU‐Net to obtain final complex images. The SCU‐Net was compared with other algorithms including zero‐filling, GRAPPA, RAKI, finite difference complex U‐type convolutional neural network (FDCU‐Net), and CU‐Net, both qualitatively and quantitatively, using such metrics as structural similarity index, peak SNR, and normalized mean square error. Results The SCU‐Net was found to be effective in deghosting aliased edge maps even at high acceleration factors. High‐quality complex images were obtained by performing an inverse filtering on deghosted edge maps. The SCU‐Net compared favorably with other algorithms. Conclusion Using sparsified complex data, SCU‐Net offers higher reconstruction quality for regularly undersampled k‐space data. The proposed method is especially useful for phase‐sensitive MRI applications.