Abstract #4474
3D locally dependent regularization of the diffusion tensor using ICA and TGV
Gernot Reishofer 1 , Kristian Bredies 2 , Karl Koschutnig 3 , Margit Jehna 4 , Christian Langkammer 5 , David Porter 6 , and Hannes Deutschmann 4
1
Radiology, Medical University of Graz, Graz,
Styria, Austria,
2
Institute
for Mathematics and Scientific Computing, Universtiy of
Graz, Austria,
3
Psychology, Universtiy of
Graz, Austria,
4
Neuroradiology,
Medical University of Graz, Austria,
5
Neurology,
Medical University of Graz, Austria,
6
Siemens
AG, Healthcare Sector, MR R&D, Germany
It has been shown recently, that spatially dependent
regularization of the diffusion tensor applied on
readout-segmented echo planar imaging (rs-EPI) with 2D
navigator-based reacquisition significantly improves
fractional anisotropy (FA) maps and tractography. In
this work we propose a novel approach for automatic
regularizing the entire diffusion tensor utilizing a
three dimensional implementation of total generalized
variation (TGV). The evaluation of the noise
distribution of the diffusion tensor by means of ICA
allows for an automatic update of the regularization
parameter making the proposed algorithm
user-independent. Furthermore the incorporation of the
locally varying noise distribution allows for a
spatially dependent regularization.
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