text: abstract—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 realtime scenarios.