Motion-Resistant Background Subtraction Angiography with Deep Learning: Real-Time, Edge Hardware Implementation and Product Development

导管数字减影血管造影术(DSA)是20世纪80年代发展起来的一种成像技术 允许医生将血管可视化。今天，这项技术被用于微创干预， 治疗许多毁灭性的疾病，包括中风和心肌梗塞，这些疾病不成比例 影响未得到充分服务的少数族裔患者群体。 导管血管造影术是将一根小导管插入动脉，注入碘化造影剂 通过导管，当对比剂穿过患者的血管时，记录一系列X射线图像。 然而，来自骨骼和软组织的叠加X射线密度掩盖了血管的成像细节。在……里面 理想的情况下，DSA将提供单独的血管图像，不被叠加的骨骼和软组织遮挡。 事实上，在合作的清醒患者的血管造影术中，他们被指示屏息以减少运动， 数字减影血管造影术可以产生良好的图像。然而，所有自愿的、呼吸的或心脏的DSA图像都会显著退化 考试期间发生的运动。在常规的临床实践中，丢弃和重复血管造影术是很常见的。 由于过度运动而进行的收购。在病人无法保持静止的情况下，这可能是由于 呼吸困难或急性中风的痛苦，运动退化的DSA成像质量差会增加 中风血栓清除和心脏支架置入术等复杂手术的风险。 我们开发了一种深度学习算法，即使在实质性的环境下也能执行DSA任务 动议。我们利用了基于Vision-Transformer的尖端网络架构，该架构经过优化以使用空间 和图像中的时间信息来识别血管并将它们与其他X射线密度分开 例如骨骼和软组织。此外，我们开发了一种新的数据增强机制来训练这一点 数据饥饿型神经网络在患者移动过程中的性能优于DSA和其他基于U-Net的架构。 在这项拨款申请中，我们建议在面向产品、低延迟 EDGE硬件设备，用于实时应用于微创手术。其次，我们将验证映像 由这款尖端五金产品生产的质量。在验证步骤中，神经学、放射学和 神经外科将在真实患者身上同时查看我们的深度学习血管成像技术和DSA的结果 血管造影术完成后的数据。在我们的资金期结束时，我们将提供经过验证的低延迟EDGE X光引导下深度学习实时血管成像算法的硬件实现 干预措施，将在未来的工作中集成到血管造影机中。

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.