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Publication Detail
Combining HARDI datasets with more than one b-value improves diffusion MRI-based cortical parcellation
  • Publication Type:
    Conference presentation
  • Publication Sub Type:
  • Authors:
    Nagy Z, Ganepola T, Sereno M, Weiskopf N, Alexander D
  • Date:
  • Name of Conference:
    ISMRM/ESMRMB Joint Annual Meeting
  • Conference place:
    Milan, Itlay
  • Conference start date:
  • Conference finish date:
  • Keywords:
    MRI, diffusion MRI, Cortex, Parcellation
  • Conference URL:
These methods are of interest for neuroscientists and neuroimaging experts interested in in–vivo, MRI–based parcellation of the cortex. Purpose: In–vivo parcellation of the human cerebral cortex has received much interest. Previous investigators used or T1/T2–weighted images or maps of T1 relaxation times to estimate the extent of cortical myelination. Others explored the utility high angular resolution diffusion–weighted imaging (HARDI) fit the data to specific models and reported layer–specific heterogeneity in the cortex, which may be used to identify cortical areas. Others have used the HARDI data in a model–free fashion by spherical harmonic decomposition to characterise the underlying tissue. In a similar fashion, we used a vector of 27 orientationally invariant HARDI features as a tissue fingerprint. In the present study we investigate whether the discriminative power of such a fingerprint could be increased. Rather than using a 1D vector we propose to construct a 2D fingerprint matrix, where the 2nd dimension encodes b–value. Given that varying b–values probe different aspects of tissue microstructure, the expectation is that data from differing b–values will parcellate differently. The aim of this study is to investigate whether combining data from different b–values improves the parcellation.
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