  CT is commonly used in orthopedic procedures. MRI is used along with CT to
identify muscle structures and diagnose osteonecrosis due to its superior soft
tissue contrast. However, MRI has poor contrast for bone structures. Clearly,
it would be helpful if a corresponding CT were available, as bone boundaries
are more clearly seen and CT has standardized (i.e., Hounsfield) units.
Therefore, we aim at MR-to-CT synthesis. The CycleGAN was successfully applied
to unpaired CT and MR images of the head, these images do not have as much
variation of intensity pairs as do images in the pelvic region due to the
presence of joints and muscles. In this paper, we extended the CycleGAN
approach by adding the gradient consistency loss to improve the accuracy at the
boundaries. We conducted two experiments. To evaluate image synthesis, we
investigated dependency of image synthesis accuracy on 1) the number of
training data and 2) the gradient consistency loss. To demonstrate the
applicability of our method, we also investigated a segmentation accuracy on
synthesized images.
