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DTI Atlases Evaluations

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Abstract

The cerebral atlas of diffusion tensor magnetic resonance image (DT-MRI, shorted as DTI) is one of the key issues in neuroimaging research. It is crucial for comparisons of neuronal structural integrity and connectivity across populations. Usually, the atlas is constructed by iteratively averaging the registered individual image. In tradition, the fuzzy group average image is easily generated in the initial stage, which is harmful to providing clear guidance for subsequent registration, to the performance of the final atlas. To solve this problem, an improved unbiased DTI atlas construction algorithm based on adaptive weights is proposed in this paper. The adaptive weighted strategy based on diffeomorphic deformable tensor registration is introduced. At the same time, the distance measure for tensors is used as a constraint condition, which ensures the unbiasedness of the atlas. Then, using 77 DTIs from the dataset in http://www.brain-development.org, three study-specific atlases, i.e. the constructed atlases of the proposed algorithm and two open-sourced algorithms (DTIAtlasBuilder and DTI-TK), are compared with two standardized atlases (IIT v. 4.1 and NTU-DSI-122-DTI). The performances of the atlases were evaluated in spatial normalization way with six region-based criteria (including Euclidean distances between diffusion tensors, Euclidean distances of the deviatoric tensors, standard deviation, overlaps of eigenvalue-eigenvector, cross-correlations and three sets angles of eigenvalue-eigenvector pairs between diffusion tensors) and three fiber-based criteria (including distances between fiber bundles, angles between fiber bundles and fiber property profile-based criteria). The experimental results showed that the overall performances of the study-specific atlases are better than those of the standardized atlases for specific datasets, and the comprehensive performance of the improved algorithm proposed in this paper is the best.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (62071384), the Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-311) and the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University of China (CX2020172).

Availability of Data and Material

The diffusion MRI data (http://www.brain-development.org) from Hammersmith hospital in London, UK is chosen in this paper.

Funding

This work was supported by the National Natural Science Foundation of China (62071384), the Natural Science Basic Research Plan in Shaanxi Province of China (2019JM-311) and the Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University of China (CX2020172).

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Authors and Affiliations

  1. School of Electronics and Information, Northwestern Polytechnical University, Xi’an, 710072, China

    Yi Wang, Maowen Xu, Lijun Geng, Yi Zhao, Zhe Guo & Yangyu Fan

  2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, China

    Yilong Niu

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  1. Yi Wang
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  2. Maowen Xu
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  3. Lijun Geng
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Contributions

In this paper, we propose an improved unbiased DTI atlas construction algorithm based on adaptive weights. The adaptive weighted strategy based on diffeomorphic deformable tensor registration is introduced. At the same time, the distance measure for tensors is used as a constraint condition, which ensures the unbiasedness of the atlas. Then, using 77 DTIs, three study-specific atlases, i.e. the constructed atlases of the proposed algorithm and two open-sourced algorithms (DTIAtlasBuilder and DTI-TK), are compared with two standardized atlases (IIT v. 4.1 and NTU-DSI-122-DTI). The performances of the atlases were evaluated in spatial normalization way with six region-based criteria (including Euclidean distances between diffusion tensors, Euclidean distances of the deviatoric tensors, standard deviation, overlaps of eigenvalue-eigenvector, cross-correlations and three sets angles of eigenvalue-eigenvector pairs between diffusion tensors) and three fiber-based criteria (including distances between fiber bundles, angles between fiber bundles and fiber property profile-based criteria).

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Correspondence to Yilong Niu.

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Wang, Y., Xu, M., Geng, L. et al. DTI Atlases Evaluations. Neuroinform 20, 327–351 (2022). https://doi.org/10.1007/s12021-021-09521-y

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