Abstract #4472
Direct Reconstruction of the Average Diffusion Propagator with Simultaneous Compressed-Sensing-Accelerated Diffusion Spectrum Imaging and Image Denoising by Means of Total Generalized Variation Regularization
Vladimir Golkov 1,2 , Marion I. Menzel 1 , Tim Sprenger 1,3 , Mohamed Souiai 2 , 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
Reconstruction of diffusion-weighted images (DWIs) in
diffusion MRI is usually done independently for each DWI,
without exploiting structural correlations between the
DWIs. In this work, we propose direct reconstruction of
the average diffusion propagator (directly from k-space
data), taking advantage of the DWIs being linked
together via their Fourier relationship with the average
propagator space, while regularization using
five-dimensional total generalized variation (TGV) along
both image space and diffusion space is applied. The
results demonstrate the ability of the method to
reconstruct q-space-undersampled data in a compressed
sensing framework, simultaneously denoising the data.
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