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

具有深度学习的抗运动背景减影血管造影：实时、边缘硬件实施和产品开发

• 批准号: 10602275
• 负责人: Sameer A Ansari
• 金额: $ 25.69万
• 依托单位: CLEARVOYA LLC
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10602275
• 关键词: 3-Dimensional; Address; Adopted; Adoption; Algorithms; Angiography; Applications Grants; Architecture; Arteries; Artificial Intelligence platform; Awareness; Bite; Blinded; Blood Vessels; Bone Density; Bone Tissue; Breathing; Buffers; Cardiac; Catheters; Characteristics; Clinical; Coagulation Process; Complex; Computer software; Data; Data Set; Devices; Digital Subtraction Angiography; Disease; Dissemination and Implementation; Distress; Environment; Equipment; Evaluation; Excision; Feedback; Foundations; Funding; Future; Goals; Hospitals; Image; Imaging Techniques; Injections; Intention; Intervention; Legal patent; Masks; Memory; Modeling; Morphologic artifacts; Motion; Myocardial Infarction; Neurology; Pathology; Patients; Performance; Physicians; Procedures; Protocols documentation; Pythons; Radiology Specialty; Resistance; Risk; Roentgen Rays; Safety; Series; Side; Site; Speed; Stents; Stream; Stroke; System; Techniques; Technology; TensorFlow; Time; Training; Validation; Vascular Diseases; Vendor; Vision; Visualization; Work; X-Ray Medical Imaging; acute stroke; awake; blood vessel visualization; bone; clinical practice; deep learning; deep learning algorithm; density; diagnosis standard; heart motion; image guided; improved; innovation; minimally invasive; network architecture; neural network; neurosurgery; novel; patient population; patient safety; product development; prospective; prototype; respiratory; serial imaging; soft tissue; stroke intervention; temporal measurement; time use; underserved minority

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.

导管数字减影血管造影 (DSA) 是一种于 20 世纪 80 年代开发的成像技术，旨在 如今，这项技术已用于微创干预。 治疗多种破坏性病症，包括中风和心肌梗塞，这些疾病 影响服务不足的少数族裔患者群体。 导管血管造影是通过将小导管插入动脉并注射碘造影剂来进行的 通过导管，并在造影剂穿过患者血管时记录一系列 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。 理想的条件下，DSA 将提供单独的血管图像，不受叠加的骨骼和软组织的影响。 事实上，在对清醒患者进行血管造影时，要求患者屏住呼吸以减少运动， DSA 可以产生出色的图像，但是，所有自主、呼吸或心脏图像都会显着降低 DSA 图像的质量。 在常规临床实践中，通常会放弃并重复血管造影。 由于过度运动而导致的采集。在患者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，运动降级 DSA 成像的质量差会增加 复杂手术的风险，例如中风血栓清除和心脏支架置入术。 我们开发了一种深度学习算法，即使在大量数据的情况下也可以执行 DSA 任务。 我们利用基于 Vision-Transformer 的尖端网络架构，该架构针对空间使用进行了优化。 和图像中的时间信息来识别血管并将其与其他 X 射线密度分开 此外，我们还开发了一种新颖的数据增强机制来训练它。 在患者运动过程中，需要大量数据的神经网络的性能优于 DSA 和基于 U-Net 的替代架构。 在本次拨款申请中，我们建议在面向产品的、低延迟的、 用于微创手术实时应用的边缘硬件设备其次，我们将验证图像。 在验证步骤中，神经病学、放射学和生理学方面的质量。 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造影技术与 DSA 的结果 血管造影完成后的数据在我们的资助期结束时，我们将提供经过验证的低延迟边缘。 我们的深度学习血管造影算法的硬件实现，可在 X 射线引导期间实时使用 干预措施，将在未来的工作中集成到血管造影机中。