Partial discreteness

Partial discreteness: A novel prior for magnetic resonance image reconstruction

Ramos-Llorden, Gabriel (Universiteit Antwerpen)
den Dekker, A.J. (TU Delft Team Raf Van de Plas; Universiteit Antwerpen)
Sijbers, Jan (Universiteit Antwerpen)

2017

An important factor influencing the quality of magnetic resonance (MR) images is the reconstruction method that is employed, and specifically, the type of prior knowledge that is exploited during reconstruction. In this work, we introduce a new type of prior knowledge, partial discreteness (PD), where a small number of regions in the image are assumed to be homogeneous and can be well represented by a constant magnitude. In particular, we mathematically formalize the partial discreteness property based on a Gaussian Mixture Model (GMM) and derive a partial discreteness image representation that characterizes the salient features of partially discrete images: a constant intensity in homogeneous areas and texture in heterogeneous areas. The partial discreteness representation is then used to construct a novel prior dedicated to the reconstruction of partially discrete MR images. The strength of the proposed prior is demonstrated on various simulated and real k-space data-based experiments with partially discrete images. Results demonstrate that the PD algorithm performs competitively with state-of-the-art reconstruction methods, being flexible and easy to implement.

Gaussian Mixture Model
MRI reconstruction
partial discreteness
segmentation
sparsity

http://resolver.tudelft.nl/uuid:b4689fb7-dc29-4df0-a488-bdeefb2e2af7

https://doi.org/10.1109/TMI.2016.2645122

0278-0062

IEEE Transactions on Medical Imaging, 36 (5), 1041-1053

Institutional Repository

journal article

© 2017 Gabriel Ramos-Llorden, A.J. den Dekker, Jan Sijbers