Retrospective distortion correction for 3D MR diffusion tensor microscopy using mutual information and Fourier deformations.

Abstract

Magnetic resonance diffusion tensor imaging (DTI) can be complicated by distortions that contribute to errors in tissue characterization and loss of fine structures. This work presents a correction scheme based on retrospective registration via mutual information (MI), using Fourier transform (FT)-based deformations to enhance the reliability of the entropy-based image registration. The registration methodology is applied to correct distortions in 3D high-resolution DTI datasets, incorporating a complete set of affine deformations. The results demonstrate that the proposed methodology can consistently and significantly reduce the number of misregistered pixels, leading to marked improvement in the visualization of internal brain white matter (WM) structure via DTI. Post-registration analysis revealed that eddy-current effects cannot fully account for the observed image distortions. Combined, these findings support the non-model-based, postprocessing approach for correcting distortions, and demonstrate the advantages of combining FT-based deformations and MI registration to enhance the practical utility of DTI.