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

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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 onex-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.

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Notes

  1. 1.

    These times were calculated on a 1.66 GHz processor dual core Intel machine with 2 GB of RAM. CSD is a non-linear method that takes several iteration to perform the constrained regularization, which goes back and forth between at least 300 points on the sphere and the order 8 SH representation. This can be greedy and in our implementation takes approximately 0.5–1 s per voxel. This time can obviously be improved by parallelizing the code and changing the parameters of CSD regularization (less iteration and faster stopping criteria).

  2. 2.

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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).

Author information

Authors and Affiliations

  1. Center for Neuroimmunology, Department of Neurosciences, Institut Biomedical Research August Pi Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Barcelona, Spain

    Vesna Prčkovska

  2. Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Bart M. ter Haar Romeny & Anna Vilanova

  3. NeuroSpin, CEA Saclay, Gif-sur-Yvette Cedex, France

    Cyril Poupon

  4. Computer science department, Université de Sherbrooke, Sherbrooke, QC, Canada

    Maxime Descoteaux

Authors
  1. Vesna Prčkovska

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  2. Maxime Descoteaux

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  3. Cyril Poupon

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  4. Bart M. ter Haar Romeny

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  5. Anna Vilanova

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Corresponding author

Correspondence toVesna Prčkovska.

Editor information

Editors and Affiliations

  1. Dept. Computer Science, Brown University, Providence, Rhode Island, USA

    David H. Laidlaw

  2. Department of Biomedical Engineering, Einhoven University of Technology, Eindhoven, 5600 MB, Netherlands

    Anna Vilanova

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Prčkovska, V., Descoteaux, M., Poupon, C., Romeny, B.M.t.H., Vilanova, A. (2012). Classification Study of DTI and HARDI Anisotropy Measures for HARDI Data Simplification. In: Laidlaw, D., Vilanova, A. (eds) New Developments in the Visualization and Processing of Tensor Fields. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27343-8_12

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