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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