Assessing the Accuracy of Spatial Normalization of Diffusion Tensor Imaging Data in the Presence of Image Artifacts

Anton Orlichenko¹, Robert J. Dawe², Huiling Peng², Konstantinos Arfanakis²

Use of diffusion tensor imaging (DTI) data with minimal image artifacts may enhance the accuracy of inter-subject spatial normalization. This effect was investigated by comparing the coherence of primary eigenvectors after normalizing separately a) data with minimal artifacts, and b) data with typical field inhomogeneity-related artifacts, acquired on the same subjects. Tensors derived from data with minimal artifacts were found to have higher primary eigenvector coherence in white matter, compared to tensors derived from data contaminated with image artifacts. These results demonstrate that achieving the most accurate spatial normalization of DTI data requires minimization of image artifacts.