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.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
