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Parameterization of White Matter Manifold-Like Structures Using Principal Surfaces

Author

Listed:
  • Chen Yue
  • Vadim Zipunnikov
  • Pierre-Louis Bazin
  • Dzung Pham
  • Daniel Reich
  • Ciprian Crainiceanu
  • Brian Caffo

Abstract

In this article, we are concerned with data generated from a diffusion tensor imaging (DTI) experiment. The goal is to parameterize manifold-like white matter tracts, such as the corpus callosum, using principal surfaces. The problem is approached by finding a geometrically motivated surface-based representation of the corpus callosum and visualized fractional anisotropy (FA) values projected onto the surface. The method also applies to any other diffusion summary. An algorithm is proposed that (a) constructs the principal surface of a corpus callosum; (b) flattens the surface into a parametric two-dimensional (2D) map; and (c) projects associated FA values on the map. The algorithm is applied to a longitudinal study containing 466 diffusion tensor images of 176 multiple sclerosis (MS) patients observed at multiple visits. For each subject and visit, the study contains a registered DTI scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation studies demonstrate fast convergence and robust performance of the algorithm under a variety of challenging scenarios.

Suggested Citation

  • Chen Yue & Vadim Zipunnikov & Pierre-Louis Bazin & Dzung Pham & Daniel Reich & Ciprian Crainiceanu & Brian Caffo, 2016. "Parameterization of White Matter Manifold-Like Structures Using Principal Surfaces," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1050-1060, July.
  • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:515:p:1050-1060
    DOI: 10.1080/01621459.2016.1164050
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    References listed on IDEAS

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    1. Caffo, Brian S. & Crainiceanu, Ciprian M. & Deng, Lijuan & Hendrix, Craig W., 2008. "A Case Study in Pharmacologic Colon Imaging Using Principal Curves in Single-Photon Emission Computed Tomography," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1470-1480.
    2. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    3. Jeff Goldsmith & Ciprian M. Crainiceanu & Brian Caffo & Daniel Reich, 2012. "Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(3), pages 453-469, May.
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    Cited by:

    1. Kun Meng & Ani Eloyan, 2021. "Principal manifold estimation via model complexity selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 369-394, April.

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