Spherical Ridgelets for Multi-Diffusion-Tensor Refinement - Concept and Evaluation

Simon Koppers, Thomas Schultz und Dorit Merhof
In proceedings of Bildverarbeitung für die Medizin, 2015
 

Abstract

High Angular Resolution Diffusion Imaging improved many neurosurgical areas due to its ability to represent complex intra-voxel structures, but is limited for clinical usability caused by long acquisition times and high noise. To transcend these limits our work addresses these problems combining a state-of-the-art multi diffusion tensor model enhanced with spherical ridgelets. Spherical ridgelets are able to reconstruct a signal with few measured directions utilizing compressed sensing. This concept shows that a combination of spherical ridgelets with a multi diffusion tensor model can improve the accuracy for low signal to noise ratios and the applicability using less than 15 measurements per voxel.

Bilder

Bibtex

@INPROCEEDINGS{Koppers:BVM2015,
     author = {Koppers, Simon and Schultz, Thomas and Merhof, Dorit},
      title = {Spherical Ridgelets for Multi-Diffusion-Tensor Refinement - Concept and Evaluation},
  booktitle = {Bildverarbeitung f{\"u}r die Medizin},
       year = {2015},
   abstract = {High Angular Resolution Diffusion Imaging improved many neurosurgical areas due to its ability to
               represent complex intra-voxel structures, but is limited for clinical usability caused by long
               acquisition times and high noise.
               To transcend these limits our work addresses these problems combining a state-of-the-art multi
               diffusion tensor model enhanced with spherical ridgelets. Spherical ridgelets are able to
               reconstruct a signal with few measured directions utilizing compressed sensing.
               This concept shows that a combination of spherical ridgelets with a multi diffusion tensor model can
               improve the accuracy for low signal to noise ratios and the applicability using less than 15
               measurements per voxel.}
}