Abstract
High angular resolution diffusion imaging (HARDI) is able to capture the water diffusion pattern in areas of complex intravoxel fiber configurations. However, compared to diffusion tensor imaging (DTI), HARDI adds extra complexity (e.g., high post-processing time and memory costs, nonintuitive visualization). Separating the data into Gaussian and non-Gaussian areas can allow to use complex HARDI models just when it is necessary. We study HARDI anisotropy measures as classification criteria applied to different HARDI models. The chosen measures are fast to calculate and provide interactive data classification. We show that increasing b-value and number of diffusion measurements above clinically accepted settings does not significantly improve the classification power of the measures. Moreover, denoising enables better quality classifications even with low b-values and low sampling schemes. We study the measures quantitatively on an ex-vivo crossing phantom, and qualitatively on real data under different acquisition schemes.
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Prčkovska, V., Vilanova, A., Poupon, C., ter Haar Romeny, B.M., Descoteaux, M. (2010). Fast Classification Scheme for HARDI Data Simplification. In: Davcev, D., Gómez, J.M. (eds) ICT Innovations 2009. ICT Innovations 2009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10781-8_36
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DOI: https://doi.org/10.1007/978-3-642-10781-8_36
Publisher Name: Springer, Berlin, Heidelberg
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