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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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