Classification Study of DTI and HARDI Anisotropy Measures for HARDI Data Simplification

  • Vesna Prčkovska
  • Maxime Descoteaux
  • Cyril Poupon
  • Bart M. ter Haar Romeny
  • Anna Vilanova
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

High angular resolution diffusion imaging (HARDI) captures the angular diffusion pattern of water molecules more accurately than diffusion tensor imaging (DTI). This is of importance mainly in areas of complex intra-voxel fiber configurations. However, the extra complexity of HARDI models has many disadvantages that make it unattractive for clinical applications. One of the main drawbacks is the long post-processing time for calculating the diffusion models. Also intuitive and fast visualization is not possible, and the memory requirements are far from modest. Separating the data into anisotropic-Gaussian (i.e., modeled by DTI) and non-Gaussian areas can alleviate some of the above mentioned issues, by using complex HARDI models only when necessary. This work presents a study of DTI and HARDI anisotropy measures applied as classification criteria for detecting non-Gaussian diffusion profiles. We quantify the classification power of these measures using a statistical test of receiver operation characteristic (ROC) curves applied on ex-vivo ground truth crossing phantoms. We show that some of the existing DTI and HARDI measures in the literature can be successfully applied for data classification to the diffusion tensor or different HARDI models respectively. The chosen measures provide fast data classification that can enable data simplification. We also show that increasing the b-value and number of diffusion measurements above clinically accepted settings does not significantly improve the classification power of the measures. Moreover, we show that a denoising pre-processing step improves the classification. This denoising enables better quality classifications even with low b-values and low sampling schemes. Finally, the findings of this study are qualitatively illustrated on real diffusion data under different acquisition schemes.

Keywords

Apparent Diffusion Coefficient Fractional Anisotropy Diffusion Tensor Imaging Mean Diffusivity Anisotropy Measure 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank Alard Roebroeck from Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology, Maastricht University, The Netherlands and Pim Pullens from Brain Innovation B.V., Maastricht, The Netherlands for providing us with in-vivo datasets. This study was financially supported by the VENI program of the Netherlands Organization for Scientific Research NWO (Anna Vilanova) and by the Netherlands Organization for Scientific Research (NWO), project number 643.100.503 MFMV (Vesna Prčkovska).

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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  • Vesna Prčkovska
    • 1
  • Maxime Descoteaux
    • 4
  • Cyril Poupon
    • 3
  • Bart M. ter Haar Romeny
    • 2
  • Anna Vilanova
    • 2
  1. 1.Center for Neuroimmunology, Department of Neurosciences, Institut Biomedical Research August Pi Sunyer (IDIBAPS)Hospital Clinic of BarcelonaBarcelonaSpain
  2. 2.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.NeuroSpin, CEA SaclayGif-sur-Yvette CedexFrance
  4. 4.Computer science departmentUniversité de SherbrookeSherbrookeCanada

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