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