k-Space Deep Learning for Reference-free EPI Ghost Correction

Abstract--Nyquist ghost artifacts in EPI images are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high-field MRI due to the nonlinear and time-varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be transformed into k -space data interpolation problem that can be solved using the annihilating filter-based low-rank Hankel structured matrix completion approach (ALOHA). Another recent discovery has shown that the deep convolutional neural network is closely related to the data-driven Hankel matrix decomposition. By synergistically combining these findings, here we propose a k -space deep learning approach that immediately corrects the k -space phase mismatch without a reference scan. Reconstruction results using 7T in vivo data showed that the proposed reference-free k -space deep learning approach for EPI ghost correction significantly improves the image quality compared to the existing methods, and the computing time is several orders of magnitude faster . Echo-planar imaging (EPI) is one of the widely used MR imaging sequences. This sequence is fast because the wholek - space data is acquired through a single RF pulse by alternating the magnetic field direction in the even and odd lines. This short scan provides high temporal resolution and has therefore been used in many imaging modalities such as functional MRI, diffusion-weighted imaging (DWI) and so on.