Aliasing Artefact Suppression in Machine Learning MRI Reconstruction for Random Phase-Encode Undersampling

Motivation: Random phase-encode undersampling of Cartesian k-space trajectories is widely implemented in magnetic resonance imaging. However, its one-dimensional randomness inherently introduces large coherent aliasing artefacts along the undersampled direction in the reconstruction, which need to be suppressed. Goal(s): Our goal is to introduce a novel reconstruction scheme to reduce the one-dimensional undersampling-induced aliasing artefacts. Approach: We propose an intermediate-domain network tailored for operation in image-Fourier space, which utilizes the superior non-coherent properties of decoupled one-dimensional signals to reduce aliasing artifacts. Results: Experiments illustrate that the proposed method is particularly well-suited for regular Cartesian undersampling scenarios. Impact: The intermediate-domain network tailored to operate in the Image-Fourier space, can efficiently reduce aliasing artefacts by utilizing the superior incoherence property of the decoupled one-dimensional signals. This could further inspire the development of MRI reconstruction technology based on machine learning.