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Abstract #4026

Classification of Non-Gaussian Diffusion Profiles for HARDI Data Simplification

Vesna Prkovska1, Anna Vilanova1, Cyril Poupon2, Bart ter Haar Romeny1, Maxime Descoteaux3

1Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; 2NeuroSpin, CEA Saclay, Gif-sur-Yvette, France; 3Computer Science, Universit de Sherbrooke, Qubec, Canada

This work presents a HARDI study of the classification power of different anisotropy measures. This classification aims towards separating the data into three compartments: Isotropic, Gaussian and Non-Gaussian. Afterwards the data can be simplified in the first two compartments by simpler diffusion models. To quantify the classification power of the measures, ex-vivo phantom data is used, and the findings are qualitatively illustrated on real data under different b-values and gradient sampling schemes. The benefits from the data simplification are clinically attractive due to the possibility of significantly decreasing the post-processing time of the HARDI models and faster, more intuitive visualization.