Spatial normalization of diffusion tensor images based on anisotropic segmentation

Jinzhong Yang, Dinggang Shen, Chandan Misra, Xiaoying Wu, Susan Resnick, Christos Davatzikos, Ragini Verma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)

Abstract

A comprehensive framework is proposed for the spatial normalization of diffusion tensor (DT) brain images using tensor-derived tissue attributes. In this framework, the brain tissues are first classified into three categories: the white matter (WM), the gray matter (GM), and the cerebral-spinal fluid (CSF) using the anisotropy and diffusivity information derived from the full tensor. The tissue attributes obtained from this anisotropic segmentation are then incorporated into a very-high-dimensional elastic registration method to produce a spatial deformation field. Finally, the rotational component in the deformation field, together with the estimated underlying fiber direction, is used to determine an appropriate tensor reorientation. This framework has been assessed quantitatively and qualitatively based on a sequence of experiments. A simulated experiment has been performed to evaluate the accuracy of the spatial warping by examining the variation between deformation fields. To verify the tensor reorientation, especially, in the anisotropic microstructures of WM fiber tissues, an experiment has been designed to compare the fiber tracts generated from the DT template and the normalized DT subjects in some regions of interest (ROIs). Finally, this method has been applied to spatially normalize 31 subjects to a common space, the case in which there exist large deformations between subjects and the existing approaches are normally difficult to achieve satisfactory results. The average across the individual normalized DT images shows a significant improvement in signal-to-noise ratio (SNR).

Original languageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume6914
DOIs
Publication statusPublished - 2008 May 19
Externally publishedYes
EventMedical Imaging 2008: Image Processing - San Diego, CA, United States
Duration: 2008 Feb 172008 Feb 19

Other

OtherMedical Imaging 2008: Image Processing
CountryUnited States
CitySan Diego, CA
Period08/2/1708/2/19

Fingerprint

Tensors
Tissue
Fibers
Brain
Experiments
Signal to noise ratio
Anisotropy
Microstructure
Fluids

Keywords

  • Anisotropic segmentation
  • Diffusion tensor imaging
  • Registration

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Yang, J., Shen, D., Misra, C., Wu, X., Resnick, S., Davatzikos, C., & Verma, R. (2008). Spatial normalization of diffusion tensor images based on anisotropic segmentation. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE (Vol. 6914). [69140L] https://doi.org/10.1117/12.769846

Spatial normalization of diffusion tensor images based on anisotropic segmentation. / Yang, Jinzhong; Shen, Dinggang; Misra, Chandan; Wu, Xiaoying; Resnick, Susan; Davatzikos, Christos; Verma, Ragini.

Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 6914 2008. 69140L.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Yang, J, Shen, D, Misra, C, Wu, X, Resnick, S, Davatzikos, C & Verma, R 2008, Spatial normalization of diffusion tensor images based on anisotropic segmentation. in Progress in Biomedical Optics and Imaging - Proceedings of SPIE. vol. 6914, 69140L, Medical Imaging 2008: Image Processing, San Diego, CA, United States, 08/2/17. https://doi.org/10.1117/12.769846
Yang J, Shen D, Misra C, Wu X, Resnick S, Davatzikos C et al. Spatial normalization of diffusion tensor images based on anisotropic segmentation. In Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 6914. 2008. 69140L https://doi.org/10.1117/12.769846
Yang, Jinzhong ; Shen, Dinggang ; Misra, Chandan ; Wu, Xiaoying ; Resnick, Susan ; Davatzikos, Christos ; Verma, Ragini. / Spatial normalization of diffusion tensor images based on anisotropic segmentation. Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Vol. 6914 2008.
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