We propose a strategy based on residual U-Net to reconstruct MR images from undersampled multichannel data by considering both k-space and image space. Our method first imputes the missing data points in k-space by utilizing the intrinsic relationships among channels. Then, the image reconstructed from the imputed k-space data is fed to another network for spatial detail refinement. Our method does not necessarily require auto-calibration signal (ACS) and is hence less susceptible to motion-induced inconsistency between the ACS and the undersampled data. Comprehensive evaluation indicates that our method yields images with superior perceptual details.
This abstract and the presentation materials are available to members only; a login is required.