  Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in
interventional medical imaging applications due to the broad variety of
contrast of MRI combined with fast imaging of X-ray-based modalities. To fully
utilize the potential of the vast amount of existing image enhancement
techniques, the corresponding information from both modalities must be present
in the same domain. For image-guided interventional procedures, X-ray
fluoroscopy has proven to be the modality of choice. Synthesizing one modality
from another in this case is an ill-posed problem due to ambiguous signal and
overlapping structures in projective geometry. To take on these challenges, we
present a learning-based solution to MR to X-ray projection-to-projection
translation. We propose an image generator network that focuses on high
representation capacity in higher resolution layers to allow for accurate
synthesis of fine details in the projection images. Additionally, a weighting
scheme in the loss computation that favors high-frequency structures is
proposed to focus on the important details and contours in projection imaging.
The proposed extensions prove valuable in generating X-ray projection images
with natural appearance. Our approach achieves a deviation from the ground
truth of only $6$% and structural similarity measure of $0.913\,\pm\,0.005$. In
particular the high frequency weighting assists in generating projection images
with sharp appearance and reduces erroneously synthesized fine details.
