Sparse registration of diffusion weighted images

Background and objective: Registration is a critical step in group analysis of diffusion weighted images (DWI). Image registration is also necessary for construction of white matter atlases that can be used to identify white matter changes. A challenge in the registration of DWI is that the orientation of the fiber bundles should be considered in the process, making their registration more challenging than that of the scalar images. Most of the current registration methods use a model of diffusion profile, limiting the method to the used model. Methods: We propose a model-independent method for DWI registration. The proposed method uses a multi-level free-form deformation (FFD), a sparse similarity measure, and a dictionary. We also propose a synthesis K-SVD algorithm for sparse interpolation of images during the registration process. We utilize two dictionaries: analysis dictionary is learned based on diffusion signals while synthesis dictionary is generated based on image patches. The proposed multi-level approach registers anatomical structures at different scales. T-test is used to determine the significance of the differences between different methods. Results: We have shown the efficiency of the proposed approach using real data. The method results in smaller generalized fractional anisotropy (GFA) root mean square (RMS) error (0.05 improvements, p - 0.0237) and angular error (0.37 degrees improvement, p - 0.0330) compared to the large deformation diffeo-morphic metric mapping (LDDMM) method and advanced normalization tools (ANTs). Conclusion: Sparse registration of diffusion signals enables registration of diffusion weighted images without using a diffusion model. (C) 2017 Elsevier B.V. All rights reserved.