  Motion artifacts can compromise the diagnostic value of computed tomography
(CT) images. Motion correction approaches require a per-scan estimation of
patient-specific motion patterns. In this work, we train a score-based model to
act as a probability density estimator for clean head CT images. Given the
trained model, we quantify the deviation of a given motion-affected CT image
from the ideal distribution through likelihood computation. We demonstrate that
the likelihood can be utilized as a surrogate metric for motion artifact
severity in the CT image facilitating the application of an iterative,
gradient-based motion compensation algorithm. By optimizing the underlying
motion parameters to maximize likelihood, our method effectively reduces motion
artifacts, bringing the image closer to the distribution of motion-free scans.
Our approach achieves comparable performance to state-of-the-art methods while
eliminating the need for a representative data set of motion-affected samples.
This is particularly advantageous in real-world applications, where patient
motion patterns may exhibit unforeseen variability, ensuring robustness without
implicit assumptions about recoverable motion types.
