Sparse registration of diffusion weighted images. (November 2017)

• Record Type: Journal Article
• Authors: Afzali, Maryam; Fatemizadeh, Emad; Soltanian-Zadeh, Hamid
• Is Part Of: Computer methods and programs in biomedicine. Volume 151(2017)
• Page Start: 33
• Page End: 43
• Publication Date: 2017-11
• Subjects: Diffusion weighted imaging -- Image registration -- Image interpolation -- Sparse representation -- K-SVD algorithm; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.08.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Registration is one of the most important steps in group analysis of DWI. A sparse similarity measure is proposed and a dictionary is learnt from DWI. K-SVD algorithm is used for interpolation of images between different levels. Two kinds of sparse learning are utilized; synthesis and analysis. The efficiency of the proposed algorithm has been shown using real data. 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.