Echo Planar Imaging (EPI) is a MRI acquisition technique that is the backbone of widely used investigation techniques in neuroscience like, e.g., Diffusion Tensor Imaging (DTI). While EPI offers considerable reduction of the acquisition time one major drawback is its high sensitivity to susceptibility artifacts. Susceptibility differences between soft tissue, bone and air cause geometrical distortions and intensity modulations of the EPI data. These susceptibility artifacts severely com- plicate the fusion of micro-structural information acquired with EPI and conventionally acquired structural information. In this paper we introduce a new tool for hyperelastic susceptibility correction of DTI data termed HySCO that is integrated into the Statistical Parametric Mapping (SPM) software as a toolbox. Our new correction pipeline is based on two datasets acquired with reversed phase encoding gradients. For the correction, we integrated the variational image registration approach by Ruthotto et al. [1] into the SPM batch mode. We briefly review the model, discuss involved parameter settings and exemplarily demonstrate HySCO’s effectiveness on a human brain DTI dataset.