2019 JRNL PP Chen Qini - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Accelerating the data acquisition of dynamic
magnetic resonance imaging leads to a challenging illposed
inverse problem, which has received great interest
from both the signal processing and machine learning
communities over the last decades. The key ingredient to
the problem is how to exploit the temporal correlations of
the MR sequence to resolve aliasing artifacts. Traditionally,
such observation led to a formulation of an optimization
problem, which was solved using iterative algorithms.
Recently, however, deep learning-based approaches have
gained significant popularity due to their ability to solve
general inverse problems. In this paper, we propose a
unique, novel convolutional recurrent neural network architecture
which reconstructs high quality cardiac MR images
from highly undersampled k-space data by jointly exploiting
the dependencies of the temporal sequences as well as
the iterative nature of the traditional optimization algorithms.
In particular, the proposed architecture embeds the
structure of the traditional iterative algorithms, efficiently
modeling the recurrence of the iterative reconstruction
stages by using recurrent hidden connections over such
iterations. In addition, spatio–temporal dependencies are
simultaneously learnt by exploiting bidirectional recurrent
hidden connections across time sequences. The proposed
method is able to learn both the temporal dependence and
the iterative reconstruction process effectively with only a
very small number of parameters, while outperforming current
MR reconstructionmethods in terms of reconstruction
accuracy and speed.