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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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