  Cerebral X-ray digital subtraction angiography (DSA) is the standard imaging
technique for visualizing blood flow and guiding endovascular treatments. The
quality of DSA is often negatively impacted by body motion during acquisition,
leading to decreased diagnostic value. Time-consuming iterative methods address
motion correction based on non-rigid registration, and employ sparse key points
and non-rigidity penalties to limit vessel distortion. Recent methods alleviate
subtraction artifacts by predicting the subtracted frame from the corresponding
unsubtracted frame, but do not explicitly compensate for motion-induced
misalignment between frames. This hinders the serial evaluation of blood flow,
and often causes undesired vasculature and contrast flow alterations, leading
to impeded usability in clinical practice. To address these limitations, we
present AngioMoCo, a learning-based framework that generates motion-compensated
DSA sequences from X-ray angiography. AngioMoCo integrates contrast extraction
and motion correction, enabling differentiation between patient motion and
intensity changes caused by contrast flow. This strategy improves registration
quality while being substantially faster than iterative elastix-based methods.
We demonstrate AngioMoCo on a large national multi-center dataset (MR CLEAN
Registry) of clinically acquired angiographic images through comprehensive
qualitative and quantitative analyses. AngioMoCo produces high-quality
motion-compensated DSA, removing motion artifacts while preserving contrast
flow. Code is publicly available at https://github.com/RuishengSu/AngioMoCo.
