A partial Fourier acquisition has been widely used for fast MR imaging. To reduce the truncation artifacts in partial Fourier image, Homodyne reconstruction is often used, and it exploits the conjugate symmetry in real-valued signal to recover the full k-space. However, the MR signal is complex-valued. Artifacts are commonly observed in Homodyne images in the regions of rapid phase change due to the interference of imaginary components of adjacent pixels. In this work, we proposed a modified Homodyne reconstruction to reduce the conventional Homodyne artifacts and truncation artifacts by using a high-resolution phase from a pre-trained deep-learning network.
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Wang et al. (2024) studied this question.