Abstract #4471
Semi-Joint Reconstruction for Diffusion MRI Denoising Imposing Similarity of Edges in Similar Diffusion-Weighted Images
Vladimir Golkov 1,2 , Marion I. Menzel 1 , Tim Sprenger 1,3 , Axel Haase 3 , Daniel Cremers 2 , and Jonathan I. Sperl 1
1
Diagnostics & Biomedical Technologies -
Europe, GE Global Research, Garching n. Munich, Germany,
2
Department
of Computer Science, Technische Universitt Mnchen,
Garching n. Munich, Germany,
3
Institute
of Medical Engineering, Technische Universitt Mnchen,
Garching n. Munich, Germany
Recently, Joint Reconstruction has been proposed by
Haldar et al. for SNR enhancement of diffusion-weighted
images (DWIs), performing edge-preserving denoising by
imposing identical edge constraints to all DWIs. In this
work, we propose Semi-Joint Reconstruction to allow
individual edges for each DWI in order to account for
the fact that distinct DWIs can look quite dissimilarly,
whereas similar DWIs contain common edge structures.
Individual edge maps necessitate edge map denoising, for
which we use DWI similarity weightings, truncated
singular value decomposition of DWIs, and shearlet-based
edge detectors. Results are comparable to Joint
Reconstruction, but more stable to regularization
parameter choice.
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