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.
@article{arxiv.1812.05405,
title = {Maximum Likelihood Estimation of Head Motion using Epipolar Consistency},
author = {Alexander Preuhs and Nishant Ravikumar and Michael Manhart and Bernhard Stimpel and Elisabeth Hoppe and Christopher Syben and Markus Kowarschik and Andreas Maier},
journal= {arXiv preprint arXiv:1812.05405},
year = {2018}
}