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Purpose: Compressed sensing MRI (CS-MRI) from single and parallel coils isone of the powerful ways to reduce the scan time of MR imaging with performanceguarantee. However, the computational costs are usually expensive. This paperaims to propose a computationally fast and accurate deep learning algorithm forthe reconstruction of MR images from highly down-sampled k-space data.  Theory: Based on the topological analysis, we show that the data manifold ofthe aliasing artifact is easier to learn from a uniform subsampling patternwith additional low-frequency k-space data. Thus, we develop deep aliasingartifact learning networks for the magnitude and phase images to estimate andremove the aliasing artifacts from highly accelerated MR acquisition.  Methods: The aliasing artifacts are directly estimated from the distortedmagnitude and phase images reconstructed from subsampled k-space data so thatwe can get an aliasing-free images by subtracting the estimated aliasingartifact from corrupted inputs. Moreover, to deal with the globally distributedaliasing artifact, we develop a multi-scale deep neural network with a largereceptive field.  Results: The experimental results confirm that the proposed deep artifactlearning network effectively estimates and removes the aliasing artifacts.Compared to existing CS methods from single and multi-coli data, the proposednetwork shows minimal errors by removing the coherent aliasing artifacts.Furthermore, the computational time is by order of magnitude faster.  Conclusion: As the proposed deep artifact learning network immediatelygenerates accurate reconstruction, it has great potential for clinicalapplications.

Deep artifact learning for compressed sensing and parallel MRI