  Deformable Image Registration (DIR) of MR and CT images is one of the most
challenging registration task, due to the inherent structural differences of
the modalities and the missing dense ground truth. Recently cycle Generative
Adversarial Networks (cycle-GANs) have been used to learn the intensity
relationship between these 2 modalities for unpaired brain data. Yet its
usefulness for DIR was not assessed.
  In this study we evaluate the DIR performance for thoracic and abdominal
organs after synthesis by cycle-GAN. We show that geometric changes, which
differentiate the two populations (e.g. inhale vs. exhale), are readily
synthesized as well. This causes substantial problems for any application which
relies on spatial correspondences being preserved between the real and the
synthesized image (e.g. plan, segmentation, landmark propagation). To alleviate
this problem, we investigated reducing the spatial information provided to the
discriminator by decreasing the size of its receptive fields.
  Image synthesis was learned from 17 unpaired subjects per modality.
Registration performance was evaluated with respect to manual segmentations of
11 structures for 3 subjects from the VISERAL challenge. State-of-the-art DIR
methods based on Normalized Mutual Information (NMI), Modality Independent
Neighborhood Descriptor (MIND) and their novel combination achieved a mean
segmentation overlap ratio of 76.7, 67.7, 76.9%, respectively. This dropped to
69.1% or less when registering images synthesized by cycle-GAN based on local
correlation, due to the poor performance on the thoracic region, where large
lung volume changes were synthesized. Performance for the abdominal region was
similar to that of CT-MRI NMI registration (77.4 vs. 78.8%) when using 3D
synthesizing MRIs (12 slices) and medium sized receptive fields for the
discriminator.
