Abstract #4465
Total Variation-Regularized Compressed Sensing Reconstruction for Multi-shell Diffusion Kurtosis Imaging
Jonathan I. Sperl 1 , Tim Sprenger 1,2 , Ek T. Tan 3 , Vladimir Golkov 1,4 , Marion I. Menzel 1 , Christopher J. Hardy 3 , and Luca Marinelli 3
1
GE Global Research, Munich, BY, Germany,
2
IMETUM,
Technical University Munich, Munich, BY, Germany,
3
GE
Global Research, Niskayuna, NY, United States,
4
Computer
Vision Group, Technical University Munich, Munich, BY,
Germany
In Diffusion Kurtosis Imaging (DKI) the data is sampled
in a series of concentric shells in the diffusion
encoding space (q-space). This work proposes to randomly
undersample this multi-shell data in q-space (i.e. to
acquire fewer data points) and to exploit the 1D Fourier
relation between single rays in q-space and in the
reciprocal propagator space in order to reconstruct the
missing points based on the principles of compressed
sensing using a non-cartesian total variation
regularization. The benefits of this approach in terms
of stability and accuracy of the kurtosis tensor
estimation are shown for a volunteer diffusion MR data
set using undersampling factors up to R=2.
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