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.

脑血管疾病仍然是造成死亡和严重残疾的常见原因