2018 JRNL PP Benjamin Hou - 1.pdf
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Terbatas  Irwan Sofiyan
» Gedung UPT Perpustakaan
Limited capture range, and the requirement
to provide high quality initialization for optimization-based
2-D/3-D image registration methods, can significantly
degrade the performance of 3-D image reconstruction and
motion compensation pipelines. Challenging clinical imaging
scenarios, which contain significant subject motion,
such as fetal in-utero imaging, complicate the 3-D image and
volume reconstruction process. In this paper, we present
a learning-based image registration method capable of
predicting 3-D rigid transformations of arbitrarily oriented
2-D image slices, with respect to a learned canonical atlas
co-ordinate system. Only image slice intensity information
is used to perform registration and canonical alignment,
no spatial transform initialization is required. To find
image transformations, we utilize a convolutional neural
network architecture to learn the regression function capable
of mapping 2-D image slices to a 3-D canonical atlas
space. We extensively evaluate the effectiveness of our
approach quantitatively on simulated magnetic resonance
imaging (MRI), fetal brain imagery with synthetic motion
and further demonstrate qualitative results on real fetal
MRI data where our method is integrated into a full reconstruction
and motion compensation pipeline. Our learning
based registration achieves an average spatial prediction
error of 7 mm on simulated data and produces qualitatively
improved reconstructions for heavily moving fetuses with
gestational ages of approximately 20 weeks. Our model
provides a general and computationally efficient solution to
the 2-D/3-D registration initialization problem and is suitable
for real-time scenarios.