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“School of Cognitive Sciences”

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Paper   IPM / Cognitive Sciences / 15427
School of Cognitive Sciences
  Title:   Sparse registration of diffusion weighted images
  Author(s): 
1 . M. Afzali
2 . E. Fatemizadeh
3 . H. Soltanian-Zadeh
  Status:   Published
  Journal: Computer Methods and Programs in Biomedicine
  Vol.:  151
  Year:  2017
  Pages:   33-43
  Supported by:  IPM
  Abstract:
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 ° improvement, p = 0.0330) compared to the large deformation diffeomorphic 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.

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