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.

翻译：脑部X射线数字减影血管造影（DSA）是可视化血流和指导血管内治疗的标准成像技术。DSA的质量常因采集过程中的身体运动而受到负面影响，导致诊断价值降低。耗时较长的迭代方法基于非刚性配准处理运动校正，并采用稀疏关键点与非刚性惩罚项来限制血管变形。近期方法通过从对应的非减影帧预测减影帧来减轻减影伪影，但未显式补偿帧间运动导致的错位。这阻碍了对血流的时序评估，常引发血管结构和对比剂流动的非预期改变，导致临床实用性受限。为解决上述局限，我们提出AngioMoCo——一种基于学习的框架，可从X射线血管造影生成运动补偿DSA序列。AngioMoCo整合了对比剂提取与运动校正，能够区分患者运动与对比剂流动引起的强度变化。该策略既提升了配准质量，又比基于弹性配准（elastix）的迭代方法大幅加快处理速度。我们在大型国家多中心数据集（MR CLEAN Registry）的临床血管造影图像上，通过全面的定性与定量分析验证了AngioMoCo。AngioMoCo可生成高质量运动补偿DSA，在保留对比剂流动的同时消除运动伪影。代码已开源至https://github.com/RuishengSu/AngioMoCo。