PyHySCO: GPU-Enabled Susceptibility Artifact Distortion Correction in Seconds
This work addresses a computational bottleneck for researchers and clinicians using EPI in neuroimaging, offering a significant speed improvement over existing tools.
The paper tackles the slow correction times for susceptibility artifacts in Echo-Planar Imaging by introducing PyHySCO, a GPU-enabled tool that reduces correction times to seconds while maintaining accuracy comparable to leading methods, as validated on 3T and 7T data from the Human Connectome Project.
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.