Catheter Digital Subtraction Angiography (DSA) is an imaging technique that was developed in the 1980s to allow physicians to visualize blood vessels. Today, this technology is utilized for minimally-invasive interventions that treat numerous devastating pathologies, including stroke and myocardial infarction, diseases that disproportionally impact underserved minority patient populations. Catheter angiography is performed by inserting a small catheter into an artery, injecting iodinated contrast through the catheter, and recording a series of X-Ray images as the contrast traverses the patient’s blood vessels. However, superimposed X-Ray densities from bones and soft tissues obscure the imaging details of the blood vessels. In ideal conditions, DSA will provide an image of the vessels alone, unobscured by superimposed bone and soft tissue. Indeed, during angiography of cooperative awake patients, who are instructed to hold their breath to reduce motion, DSA can produce excellent images. However, DSA images are markedly degraded by all voluntary, respiratory, or cardiac motion that occurs during the exam. During routine clinical practice, it is common to discard and repeat angiographic acquisitions due to excessive motion. In situations where patients are unable to remain still, which may be due to difficulty breathing or the distress of an acute stroke, the poor quality of motion-degraded DSA imaging increases the risk of complex procedures such as stroke clot removal and cardiac stenting. We have developed a deep learning algorithm that can perform the task of DSA even in the setting of substantial motion. We utilize a cutting edge Vision-Transformer-based network architecture, which is optimized to use the spatial and temporal information in the images to identify the blood vessels and separate them from the other X-ray densities such as bone and soft tissue. Furthermore, we have developed a novel data-augmentation mechanism to train this data-hungry neural network to outperform DSA and alternative U-Net-based architectures during patient motion. In this grant application, we propose to implement our innovative algorithm on a product-oriented, low-latency, edge hardware device for real-time application in minimally-invasive procedures. Second, we will validate the image quality produced by of this edge hardware product. In the validation step, physicians in Neurology, Radiology, and Neurosurgery will view the results of our Deep Learning Angiography technology side-by-side with DSA on real patient data after the angiogram is complete. At the end of our funding period, we will deliver a validated, low-latency, edge hardware implementation of our Deep Learning Angiography algorithm for real-time use during X-ray guided interventions, which will be integrated into angiography machines in future work.