2018 JRNL PP Abolfazl Mehranian - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
In this paper, we propose a generalized joint
sparsity regularization prior and reconstruction framework
for the synergistic reconstruction of positron emission
tomography (PET) and under sampled sensitivity encoded
magnetic resonance imaging data with the aim of improving
image quality beyond that obtained through conventional
independent reconstructions. The proposed prior improves
upon the joint total variation (TV) using a non-convex
potential function that assigns a relatively lower penalty for
the PET and MR gradients, whose magnitudes are jointly
large, thus permitting the preservation and formation of
common boundaries irrespective of their relative orientation.
The alternating directionmethod ofmultipliers (ADMM)
optimization framework was exploited for the joint PET-MR
image reconstruction. In this framework, the jointmaximum
a posteriori objective function was effectively optimized by
alternating between well-established regularized PET and
MR image reconstructions. Moreover, the dependency of
the joint prior on the PET and MR signal intensities was
addressed by a novel alternating scaling of the distribution
of the gradient vectors. The proposed prior was compared
with the separate TV and joint TV regularization methods
using extensive simulation and real clinical data. In addition,
the proposed joint prior was compared with the recently
proposed linear parallel level sets (PLSs) method using a
benchmark simulation data set. Our simulation and clinical
data results demonstrated the improved quality of the synergistically
reconstructed PET-MR images compared with
the unregularized and conventional separately regularized
methods. It was also found that the proposed prior can outperformboth
the jointTV andlinearPLSregularizationmethods
in assisting edge preservation and recovery of details,
which are otherwise impairedby noise and aliasing artifacts.
In conclusion, the proposed joint sparsity regularization
within the presented a ADMM reconstruction framework
is a promising technique, nonetheless our clinical results
showed that the clinical applicability of joint reconstruction
might be limited in current PET-MR scanners,mainly due to
the lower resolution of PET images.