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