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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