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Abstract #0570

ICTGV Regularization for Highly Accelerated Dynamic MRI

Matthias Schloegl 1 , Martin Holler 2 , Kristian Bredies 2 , Karl Kunisch 2 , and Rudolf Stollberger 1

1 Institute of Medical Engineering, Graz University of Technology, Graz, Styria, Austria, 2 Department of Mathematics and Scientific Computing, University of Graz, Graz, Styria, Austria

In this work we address the problem of undersampled dynamic MR image reconstruction from the general point-of-view of appropriate regularization for image sequences, based on the total generalized variation (TGV) functional. The extension to the dynamic scenario is achieved by infimal convolution of two suitable weighted spatio-temporal TGV functionals that automatically balance the regularity between time and space in an optimal way. This poses a very general yet computational tractable and well-studied motion model for a wide range of dynamic MR applications.

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