AngioMoCo: Learning-based Motion Correction in Cerebral Digital Subtraction Angiography

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.

Spatio-Temporal U-Net for Cerebral Artery and Vein Segmentation in Digital Subtraction Angiography

X-ray digital subtraction angiography (DSA) is widely used for vessel and/or flow visualization and interventional guidance during endovascular treatment of patients with a stroke or aneurysm. To assist in peri-operative decision making as well as post-operative prognosis, automatic DSA analysis algorithms are being developed to obtain relevant image-based information. Such analyses include detection of vascular disease, evaluation of perfusion based on time intensity curves (TIC), and quantitative biomarker extraction for automated treatment evaluation in endovascular thrombectomy. Methodologically, such vessel-based analysis tasks may be facilitated by automatic and accurate artery-vein segmentation algorithms. The present work describes to the best of our knowledge the first study that addresses automatic artery-vein segmentation in DSA using deep learning. We propose a novel spatio-temporal U-Net (ST U-Net) architecture which integrates convolutional gated recurrent units (ConvGRU) in the contracting branch of U-Net. The network encodes a 2D+t DSA series of variable length and decodes it into a 2D segmentation image. On a multi-center routinely acquired dataset, the proposed method significantly outperformed U-Net (P<0.001) and traditional Frangi-based K-means clustering (P$<$0.001). Particularly in artery-vein segmentation, ST U-Net achieved a Dice coefficient of 0.794, surpassing the existing state-of-the-art methods by a margin of 12\%-20\%. Code will be made publicly available upon acceptance.