Meeting Banner
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