Abstract
High angular resolution diffusion imaging (HARDI) improved many neurosurgical areas due to its ability to represent complex intravoxel structures, but is limited for clinical use mainly due to long acquisition times, but also due to noise.
To transcend these limits, our work addresses these problems by combining a state-of-the-art multi diffusion tensor model enhanced with spherical ridgelets. Spherical ridgelets are able to reconstruct a signal based on a limited number of measured directions by utilizing compressed sensing. This concept shows that combining spherical ridgelets with a multi diffusion tensor model can improve the accuracy in case of low signal-to-noise ratios and makes it possible to use less than 15 directional measurements per voxel.
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Koppers, S., Schultz, T., Merhof, D. (2015). Spherical Ridgelets for Multi-Diffusion Tensor Refinement. In: Handels, H., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2015. Informatik aktuell. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46224-9_75
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DOI: https://doi.org/10.1007/978-3-662-46224-9_75
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