2019 JRNL PP Yujia Zhou - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Conducting an accurate motion correction
of liver dynamic contrast-enhanced magnetic resonance
(DCE-MR) imaging remains challengingbecauseof intensity
variations caused by contrast agents. Such variations lead
to the failure of the traditional intensity-based registration
method. To address this problem, we propose a correlationweighted
sparse representation framework to separate the
contrast agent from original liver DCE-MR images. This
framework allows the robust registration of motion components
over time without intensity variances. Existing sparse
coding techniques recover a 3D image containing only
contrast agents (named contrast enhancement component)
from a manually labeled dictionary, whose column has the
same size with the original 3D volume (3D-t mode). The
high dimension of the recovery target (3D volume) and the
indistinguishabilitybetween the unenhanced and enhanced
imagesmake accurate coding difficult. In this paper,we predefine
an ideal time-intensity curve containing only contrast
agents (named contrast agent curve) and recover it from
the transpose dictionary (t-3D mode), whose column has
been updated into the original time-intensity curves. The
low dimension of the target (1D curve) and the significant
intergroup difference between contrast agent curves and non-contrast agent curves can estimate a series of pure
contrast agent curves. A “correlation-weighted” constraint
is introduced for the selection of a coding subset with more
contrast agent curves, leading to an efficient and accurate
sparse recovery process. Then, the contrast enhancement
component can be estimated by the solved sparse
coefficients’ map and the ideal curve and subtracted from
the original DCE-MRI. Finally, we register the de-enhanced
images and apply the obtained deformation fields for the
original DCE-MRI to achieve the goal of motion correction.
We conduct the experimentson both simulated and real liver
DCE-MRI data. Compared with other state-of-the-art DCEMRI
registration methods, the experimental results show
that our method achieves a better registration performance
with less computational efficiency.