  Algorithmic X-ray scatter compensation is a desirable technique in flat-panel
X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based
image translation approaches yielded promising results. As there are no physics
constraints applied to the output of the U-Net, it cannot be ruled out that it
yields spurious results. Unfortunately, those may be misleading in the context
of medical imaging. To overcome this problem, we propose to embed B-splines as
a known operator into neural networks. This inherently limits their predictions
to well-behaved and smooth functions. In a study using synthetic head and
thorax data as well as real thorax phantom data, we found that our approach
performed on par with U-net when comparing both algorithms based on
quantitative performance metrics. However, our approach not only reduces
runtime and parameter complexity, but we also found it much more robust to
unseen noise levels. While the U-net responded with visible artifacts, our
approach preserved the X-ray signal's frequency characteristics.
