Title: 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 non-linear 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.