Hyperelastic susceptibility artifact correction of DTI in SPM

Abstract

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 complicate 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 (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. 2007 into the SPM batch mode. We briefly review the model, discuss involved parameter settings and exemplarily demonstrate the effectiveness of HySCO on a human brain DTI dataset.