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Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy

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  • Fang-Cheng Yeh
  • Timothy D Verstynen
  • Yibao Wang
  • Juan C Fernández-Miranda
  • Wen-Yih Isaac Tseng

Abstract

Diffusion MRI tractography has emerged as a useful and popular tool for mapping connections between brain regions. In this study, we examined the performance of quantitative anisotropy (QA) in facilitating deterministic fiber tracking. Two phantom studies were conducted. The first phantom study examined the susceptibility of fractional anisotropy (FA), generalized factional anisotropy (GFA), and QA to various partial volume effects. The second phantom study examined the spatial resolution of the FA-aided, GFA-aided, and QA-aided tractographies. An in vivo study was conducted to track the arcuate fasciculus, and two neurosurgeons blind to the acquisition and analysis settings were invited to identify false tracks. The performance of QA in assisting fiber tracking was compared with FA, GFA, and anatomical information from T1-weighted images. Our first phantom study showed that QA is less sensitive to the partial volume effects of crossing fibers and free water, suggesting that it is a robust index. The second phantom study showed that the QA-aided tractography has better resolution than the FA-aided and GFA-aided tractography. Our in vivo study further showed that the QA-aided tractography outperforms the FA-aided, GFA-aided, and anatomy-aided tractographies. In the shell scheme (HARDI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 30.7%, 32.6%, and 24.45% of the false tracks, respectively, while the QA-aided tractography has 16.2%. In the grid scheme (DSI), the FA-aided, GFA-aided, and anatomy-aided tractographies have 12.3%, 9.0%, and 10.93% of the false tracks, respectively, while the QA-aided tractography has 4.43%. The QA-aided deterministic fiber tracking may assist fiber tracking studies and facilitate the advancement of human connectomics.

Suggested Citation

  • Fang-Cheng Yeh & Timothy D Verstynen & Yibao Wang & Juan C Fernández-Miranda & Wen-Yih Isaac Tseng, 2013. "Deterministic Diffusion Fiber Tracking Improved by Quantitative Anisotropy," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
  • Handle: RePEc:plo:pone00:0080713
    DOI: 10.1371/journal.pone.0080713
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    1. Ahmed Faraz Khan & Quadri Adewale & Sue-Jin Lin & Tobias R. Baumeister & Yashar Zeighami & Felix Carbonell & Nicola Palomero-Gallagher & Yasser Iturria-Medina, 2023. "Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson’s disease," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    2. Yali Huang & Peng-Hu Wei & Longzhou Xu & Desheng Chen & Yanfeng Yang & Wenkai Song & Yangyang Yi & Xiaoli Jia & Guowei Wu & Qingchen Fan & Zaixu Cui & Guoguang Zhao, 2023. "Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. S. Parker Singleton & Andrea I. Luppi & Robin L. Carhart-Harris & Josephine Cruzat & Leor Roseman & David J. Nutt & Gustavo Deco & Morten L. Kringelbach & Emmanuel A. Stamatakis & Amy Kuceyeski, 2022. "Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape," Nature Communications, Nature, vol. 13(1), pages 1-13, December.

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