  Open gantry C-arm systems that are placed within the interventional room
enable 3-D imaging and guidance for stroke therapy without patient transfer.
This can profit in drastically reduced time-totherapy, however, due to the
interventional setting, the data acquisition is comparatively slow. Thus,
involuntary patient motion needs to be estimated and compensated to achieve
high image quality. Patient motion results in a misalignment of the geometry
and the acquired image data. Consistency measures can be used to restore the
correct mapping to compensate the motion. They describe constraints on an
idealized imaging process which makes them also sensitive to beam hardening,
scatter, truncation or overexposure. We propose a probabilistic approach based
on the Student's t-distribution to model image artifacts that affect the
consistency measure without sourcing from motion.
