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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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