  Limited capture range, and the requirement to provide high quality
initialization for optimization-based 2D/3D image registration methods, can
significantly degrade the performance of 3D image reconstruction and motion
compensation pipelines. Challenging clinical imaging scenarios, which contain
significant subject motion such as fetal in-utero imaging, complicate the 3D
image and volume reconstruction process. In this paper we present a learning
based image registration method capable of predicting 3D rigid transformations
of arbitrarily oriented 2D 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 (CNN) architecture to learn the regression
function capable of mapping 2D image slices to a 3D 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 2D/3D registration initialization problem and is
suitable for real-time scenarios.
