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Wang Y, Jiang F, Liu Y. Spectrum-sine interpolation framework for DTI processing. Med Biol Eng Comput. 2021;60(1):279-295doi: 10.1007/s11517-021-02471-2.
Wang, Y., Jiang, F., & Liu, Y. (2022). Spectrum-sine interpolation framework for DTI processing. Medical & biological engineering & computing, 60(1), 279-295. https://doi.org/10.1007/s11517-021-02471-2
Wang, Yuanjun, et al. "Spectrum-sine interpolation framework for DTI processing." Medical & biological engineering & computing vol. 60,1 (2022): 279-295. doi: https://doi.org/10.1007/s11517-021-02471-2
Wang Y, Jiang F, Liu Y. Spectrum-sine interpolation framework for DTI processing. Med Biol Eng Comput. 2022 Jan;60(1):279-295. doi: 10.1007/s11517-021-02471-2. Epub 2021 Nov 29. PMID: 34845595.
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Med Biol Eng Comput. 2022 Jan;60(1):279-295. doi: 10.1007/s11517-021-02471-2. Epub 2021 Nov 29.

Spectrum-sine interpolation framework for DTI processing.

Medical & biological engineering & computing

Yuanjun Wang, Fan Jiang, Yu Liu

Affiliations

  1. Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China. [email protected].
  2. Institute of Medical Imaging and Engineering, University of Shanghai for Science and Technology, Shanghai, China.
  3. Department of Radiology, Shanghai Ninth People's Hospital, Shanghai JiaoTong University School of Medicine, Shanghai, China. [email protected].

PMID: 34845595 DOI: 10.1007/s11517-021-02471-2

Abstract

Diffusion tensor imaging (DTI) data interpolation is important for DTI processing, which could affect the precision and computational complexity in the process of denoising, filtering, regularization, and DTI registration and fiber tracking. In this paper, we propose a novel DTI interpolation framework named with spectrum-sine (SS) focusing on tensor orientation variation in DTI processing. Compared with the state-of-the-art DTI interpolation method using Euler angles or quaternion to represent the orientation of DTI tensors, this method does not need to convert eigenvectors into Euler angles or quaternions, but interpolates each tensor's unit eigenvector directly. The prominent merit of this tensor interpolation method lies in tensor orientation information preservation, which is different from the existing DTI tensor interpolation methods that interpolating tensor's orientation information in a scalar way. The experimental results from both synthetic and real human brain DTI data demonstrated the proposed SS interpolation scheme not only maintains the advantages of Log-Euclidean and Riemannian interpolation frameworks, such as preserving the tensor's symmetric positive definiteness and the monotonic determinant variation, but also preserve the tensor's anisotropy property which was proposed in the spectral quaternion (SQ) method.

© 2021. International Federation for Medical and Biological Engineering.

Keywords: DTI mean; Diffusion tensor MRI; Riemannian manifold; Spectrum-sine interpolation

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