TY - JOUR
T1 - Partial discreteness
T2 - A novel prior for magnetic resonance image reconstruction
AU - Ramos-Llorden, Gabriel
AU - Den Dekker, Arnold J.
AU - Sijbers, Jan
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
KW - Gaussian Mixture Model
KW - MRI reconstruction
KW - partial discreteness
KW - segmentation
KW - sparsity
UR - http://resolver.tudelft.nl/uuid:b4689fb7-dc29-4df0-a488-bdeefb2e2af7
UR - http://www.scopus.com/inward/record.url?scp=85019244746&partnerID=8YFLogxK
U2 - 10.1109/TMI.2016.2645122
DO - 10.1109/TMI.2016.2645122
M3 - Article
AN - SCOPUS:85019244746
VL - 36
SP - 1041
EP - 1053
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 5
ER -