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Abstract #0665

Novel acquisition scheme for diffusion kurtosis imaging based on compressed-sensing accelerated DSI yielding superior image quality

Tim Sprenger 1,2 , Jonathan I. Sperl 1 , Brice Fernandez 3 , Vladimir Golkov 1 , Ek Tsoon Tan 4 , Christopher Hardy 4 , Luca Marinelli 4 , Michael Czisch 5 , Philipp Saemann 5 , Axel Haase 2 , and Marion I. Menzel 1

1 GE Global Research, Munich, Germany, 2 Technical University Munich, Munich, Germany, 3 GE Healthcare, Munich, Germany, 4 GE Global Research, Niskayuna, NY, United States, 5 Max Planck Institute of Psychiatry, Munich, Germany

In Diffusion Kurtosis Imaging (DKI), the data is sampled in a series of concentric shells in the diffusion encoding space (q-space) and usu-ally suffers from low SNR. In this work a novel acquisition scheme for kurtosis imaging is presented, based on undersampled diffusion spectrum imaging (DSI) followed by a compressed sensing (CS) reconstruction of q-space. The undersampling thereby yields a compara-ble number of q-space sampling points as in standard DKI schemes whereas the CS-based denoising is shown to improve stability and accuracy of the kurtosis tensor estimation.

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