AliasNet: Alias Artefact Suppression Network for Accelerated Phase-Encode MRI

Sparse reconstruction is an important aspect of MRI, helping to reduce acquisition time and improve spatial-temporal resolution. Popular methods are based mostly on compressed sensing (CS), which relies on the random sampling of k-space to produce incoherent (noise-like) artefacts. Due to hardware constraints, 1D Cartesian phase-encode under-sampling schemes are popular for 2D CS-MRI. However, 1D under-sampling limits 2D incoherence between measurements, yielding structured aliasing artefacts (ghosts) that may be difficult to remove assuming a 2D sparsity model. Reconstruction algorithms typically deploy direction-insensitive 2D regularisation for these direction-associated artefacts. Recognising that phase-encode artefacts can be separated into contiguous 1D signals, we develop two decoupling techniques that enable explicit 1D regularisation and leverage the excellent 1D incoherence characteristics. We also derive a combined 1D + 2D reconstruction technique that takes advantage of spatial relationships within the image. Experiments conducted on retrospectively under-sampled brain and knee data demonstrate that combination of the proposed 1D AliasNet modules with existing 2D deep learned (DL) recovery techniques leads to an improvement in image quality. We also find AliasNet enables a superior scaling of performance compared to increasing the size of the original 2D network layers. AliasNet therefore improves the regularisation of aliasing artefacts arising from phase-encode under-sampling, by tailoring the network architecture to account for their expected appearance. The proposed 1D + 2D approach is compatible with any existing 2D DL recovery technique deployed for this application

Aliasing Artefact Suppression in Compressed Sensing MRI for Random Phase-Encode Undersampling

Goal: Random phase-encode undersampling of Cartesian k-space trajectories is widely implemented in compressed sensing (CS) MRI. However, its one-dimensional (1-D) randomness inherently introduces large coherent aliasing artefacts along the undersampled direction in the reconstruction and, thus, degrades the image quality. This paper proposes a novel reconstruction scheme to reduce the 1-D undersampling-induced aliasing artefacts. Methods: The proposed reconstruction progress is separated into two steps in our new algorithm. In step one, we transfer the original two-dimensional (2-D) image reconstruction into a parallel 1-D signal reconstruction procedure, which takes advantage of the superior incoherence property in the phase direction. In step two, using the new k-space data obtained from the 1-D reconstructions, we implement a follow-up 2-D CS reconstruction to produce a better solution, which exploits the inherent correlations between the adjacent lines of 1-D reconstructed signals. Results: We evaluated the performance on various cases of typical MR images, including cardiac cine, brain, foot, and angiogram at the reduction factor up to 10 and compared the results with the conventional CS method. Experiments using the proposed method demonstrated faithful reconstruction of the MR images. Conclusion: Compared with conventional method, the new method achieves more accurate reconstruction results with 2-5 dB gain in peak SNR and higher structural similarity index. Significance: The proposed method improves image quality of the reconstructions and suppresses the coherent artefacts introduced by the random phase-encode undersampling