  Purpose: We propose and evaluate a new structured low-rank method for EPI
ghost correction called Robust Autocalibrated LORAKS (RAC-LORAKS). The method
can be used to suppress EPI ghosts arising from the differences between
different readout gradient polarities and/or the differences between different
shots. It does not require conventional EPI navigator signals, and is robust to
imperfect autocalibration data.
  Methods: Autocalibrated LORAKS is a previous structured low-rank method for
EPI ghost correction that uses GRAPPA-type autocalibration data to enable
high-quality ghost correction. This method works well when the autocalibration
data is pristine, but performance degrades substantially when the
autocalibration information is imperfect. RAC-LORAKS generalizes Autocalibrated
LORAKS in two ways. First, it does not completely trust the information from
autocalibration data, and instead considers the autocalibration and EPI data
simultaneously when estimating low-rank matrix structure. And second, it uses
complementary information from the autocalibration data to improve EPI
reconstruction in a multi-contrast joint reconstruction framework. RAC-LORAKS
is evaluated using simulations and in vivo data, including comparisons to
state-of-the-art methods.
  Results: RAC-LORAKS is demonstrated to have good ghost elimination
performance compared to state-of-the-art methods in several complicated EPI
acquisition scenarios (including gradient-echo brain imaging, diffusion-encoded
brain imaging, and cardiac imaging).
  Conclusion: RAC-LORAKS provides effective suppression of EPI ghosts and is
robust to imperfect autocalibration data.
