Digital subtraction angiography (DSA) is widely used to assess vascular changes during endovascular thrombectomy, but manual comparison of image pairs is challenging and subjective. This work presents a deep learning-based method for arterial change detection in cerebral DSA by combining inter-sequence registration with a siamese spatiotemporal U-Net for joint arterial segmentation and change detection. Experiments on data from the MR CLEAN registry demonstrate that the proposed registration method significantly improves accuracy compared with the best-performing baseline (p=0.0054) and that the change-detection network consistently identifies arterial changes in successfully co-registered DSA pairs (Dice = 0.70). A preliminary reader study indicated that marking these changes can improve inter-rater agreement of extended thrombolysis in cerebral infarction (eTICI) scoring and increase the detection rate of emboli in new territory (ENT).