  Over the past decade, reversed Gradient Polarity (RGP) methods have become a
popular approach for correcting susceptibility artifacts in Echo-Planar Imaging
(EPI). Although several post-processing tools for RGP are available, their
implementations do not fully leverage recent hardware, algorithmic, and
computational advances, leading to correction times of several minutes per
image volume. To enable 3D RGP correction in seconds, we introduce PyHySCO, a
user-friendly EPI distortion correction tool implemented in PyTorch that
enables multi-threading and efficient use of graphics processing units (GPUs).
PyHySCO uses a time-tested physical distortion model and mathematical
formulation and is, therefore, reliable without training. An algorithmic
improvement in PyHySCO is its novel initialization scheme that uses 1D optimal
transport. PyHySCO is published under the GNU public license and can be used
from the command line or its Python interface. Our extensive numerical
validation using 3T and 7T data from the Human Connectome Project suggests that
PyHySCO achieves accuracy comparable to that of leading RGP tools at a fraction
of the cost. We also validate the new initialization scheme, compare different
optimization algorithms, and test the algorithm on different hardware and
arithmetic precision.
