Cerebrovascular Reserve Imaging with Simultaneous PET/MRI Using Arterial Spin Labeling and Deep Learning

使用动脉自旋标记和深度学习同时进行 PET/MRI 脑血管储备成像

• 批准号: 9789276
• 负责人: Gregory George Zaharchuk
• 金额: $ 58.63万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9789276
• 关键词: Acetazolamide; Address; Age; Artificial Intelligence; Biological; Blood flow; Brain; Brain imaging; Bypass; Cardiac; Cardiology; Carotid Stenosis; Cause of Death; Cerebrovascular Circulation; Cerebrovascular Disorders; Clinical; Clinical Trials Design; Computer Vision Systems; Consensus; Cytolysis; Data; Deposition; Diagnostic; Excision; Gadolinium; Gender; Goals; Gold; Guidelines; ImProv; Image; Imaging problem; Impairment; Individual; Intervention; Intervention Trial; Japanese Population; Label; Link; Machine Learning; Magnetic Resonance Imaging; Measurement; Measures; Meta-Analysis; Methodology; Methods; Modeling; Motion; Noise; Nutrient; Outcome; Oxygen; Patient Triage; Patients; Physiologic pulse; Physiological; Play; Positron-Emission Tomography; Premenopause; Procedures; Radiation; Reference Standards; Reproducibility; Rest; Risk; Role; Scanning; Signal Transduction; Solid; Spin Labels; Stress Tests; Stroke prevention; Techniques; Testing; Time; Training; United States; Validation; Waste Products; Water; Woman; Xenon; base; blood flow measurement; cerebral hemodynamics; cerebrovascular; cerebrovascular imaging; clinical application; cohort; convolutional neural network; deep field survey; deep learning; design; direct application; disability; evidence based guidelines; gender difference; imaging modality; improved; learning network; men; nervous system disorder; novel strategies; performance tests; prospective; response; stressor; stroke risk; systematic review; time use; tool

Cerebrovascular disease remains a common cause of death and major disability in the United States, and identifying and preventing strokes should be a high priority. Direct measurement of regional cerebral blood flow (CBF) is challeng- ing in these patients, since we do not have a non-invasive, radiation-free imaging method that has been appropriately validated against gold standard techniques. This is important, because there is compelling evidence that measuring the CBF change before and after a stress test meant to increase CBF (a measurement known as cerebrovascular reserve [CVR]) can identify patients at increased stroke risk. Stress tests have been a mainstay of the diagnostic workup of cardiology patients for many years, and we believe strongly that their use will benefit cerebrovascular disease patients as well. The goal of this project is to improve the quality of arterial spin label (ASL) MRI using deep learning, a powerful form of machine learning, that is currently undergoing tremendous progress. We will then to apply this in a prospective, adaptive validation trial against oxygen-15 water PET CBF, using simultaneous PET/MRI to minimize biological variability. Finally, we will apply this improv- ed tool to study the effects of gender on CVR and its reproducibility. Successful completion of this study will result in a validated methodology to assess CVR in cerebrovascular disease patients without the use of radiation or contrast. As such, it will provide solid, evidence-based recommendations for clinicians developing new paradigms and interventions in patients with impaired CVR.

脑血管疾病仍然是导致死亡和重大残疾的常见原因 在美国，识别和预防中风应该是一个很高的 要务局部脑血流量（CBF）的直接测量是可行的， 因为我们没有一个非侵入性的，无辐射的 已根据金标准进行适当验证的成像方法 技术.这很重要，因为有令人信服的证据表明， 测量压力测试前后的CBF变化，以增加CBF （一种称为脑血管储备[CVR]的测量方法）可以识别患者 中风风险增加。压力测试一直是诊断的支柱 多年来，我们对心脏病患者进行了检查，我们坚信， 对脑血管病患者也有好处。 本课题的目的是提高动脉自旋标记的质量 MRI使用深度学习，这是一种强大的机器学习形式， 取得了巨大的进步然后我们将把它应用在一个前瞻性的， 针对氧-15水PET CBF的适应性验证试验，同时使用 PET/MRI，以最大限度地减少生物变异性。最后，我们将运用这种即兴表演- 艾德的工具来研究性别对CVR及其重复性的影响。 成功完成本研究将产生一种经验证的方法， 评估脑血管疾病患者的CVR，而不使用放射或 对比度因此，它将提供坚实的，以证据为基础的建议， 临床医生开发新的范例和干预措施， CVR受损。