Abstract #3652
Water-Fat Separation Using a Locally Low-Rank Enforcing Reconstruction
Felix Lugauer 1 , Dominik Nickel 2 , Jens Wetzl 1 , Berthold Kiefer 2 , and Joachim Hornegger 1
1
Pattern Recognition Lab, Department of
Computer Science, Friedrich-Alexander-Universitt
Erlangen-Nrnberg, Erlangen, Germany,
2
Siemens
AG, Healthcare, Imaging & Therapy Systems, Magnetic
Resonance, Erlangen, Germany
Multi-contrast water-fat separation based on the Dixon
method is gaining importance in clinical routine. A
combination with iterative reconstruction also
addressing field inhomogeneities, relaxation and eddy
current effects is, however, not straightforward as the
optimization is rendered non-convex. Here we demonstrate
that water-fat separation can be decoupled by first
reconstructing the multiple echos using a locally
low-rank regularization. This enforces a representation
of the contrast images with as few chemical components
as possible, assuming a low-resolution phase evolution.
Both are common assumptions. The approach allows bipolar
acquisitions, varying sampling patterns across contrasts
and promises superior image quality over conventional
reconstructions.
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