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

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 nonGaussian 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 V. Prčkovska ( ) Center for Neuroimmunology, Department of Neurosciences, Institut Biomedical Research August Pi Sunyer (IDIBAPS), Hospital Clinic of Barcelona, Spain e-mail: [email protected] B.M. ter Haar Romeny A. Vilanova Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands e-mail: [email protected]; [email protected] C. Poupon NeuroSpin, CEA Saclay, Gif-sur-Yvette Cedex, France e-mail: [email protected] M. Descoteaux Computer science department, Université de Sherbrooke, Sherbrooke, QC, Canada e-mail: [email protected] D.H. Laidlaw and A. Vilanova (eds.), New Developments in the Visualization and Processing of Tensor Fields, Mathematics and Visualization, DOI 10.1007/978-3-642-27343-8 12, © Springer-Verlag Berlin Heidelberg 2012 229 230 V. Prčkovska et al. 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.