Abstract #3417
Partial discreteness: a new type of prior knowledge for MRI reconstruction
Gabriel Ramos-Llordn 1 , Hilde Segers 1 , Willem Jan Palenstijn 1 , Arnold J. den Dekker 1,2 , and Jan Sijbers 1
1
iMinds Vision-Lab, University of Antwerp,
Antwerp, Antwerp, Belgium,
2
Delft
Center for Systems and Control, Delft University of
Technology, Delft, Netherlands
In MRI reconstruction, undersampled data sets lead to
ill-posed reconstruction problems. To regularize these
problems, prior knowledge is commonly exploited. In this
work, we introduce a new type of prior knowledge,
partial discreteness, where part of the image is assumed
to be homogeneous and can be well represented by a
constant magnitude. We introduce this prior in the
common algebraic reconstruction problem and propose an
iterative algorithm to approximately solve it. It
combines a penalized least squares reconstruction with
an internal Bayesian segmentation. Results with
synthetic data demonstrate that more detailedly restored
images are obtained when partial discreteness is
exploited
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