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

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:
依托单位国家:
美国
项目类别:
财政年份:
2018
资助国家:
美国
项目状态:
已结题
起止时间:
2018-09-20 至 2022-05-31

项目摘要

项目成果

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中文摘要
翻译
脑血管疾病仍然是导致死亡和主要残疾的常见原因 在美国,识别和预防中风应该是一个很高的 优先考虑。直接测量局部脑血流量(CBF)是一种新的方法。 在这些患者中,既然我们没有一种非侵入性、无辐射的 已根据黄金标准进行适当验证的成像方法 技巧。这一点很重要,因为有令人信服的证据表明 测量旨在增加CBF的压力测试前后的CBF变化 (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 多年来心脏病患者的体检,我们坚信他们的 使用也将使脑血管疾病患者受益。 本项目的目标是提高动脉自旋标记(Asl)的质量。 使用深度学习的核磁共振成像,这是机器学习的一种强大形式,目前 正在经历巨大的进步。然后我们将把这一点应用于未来的, 对氧气-15水PET CBF的适应性验证试验,使用同步 PET/MRI以最大限度地减少生物变异性。最后,我们将运用这一即兴表演- ED工具,研究性别对CVR及其重复性的影响。 成功完成这项研究将产生一种经过验证的方法来 评估脑血管疾病患者在不使用放射或药物的情况下的脑血管阻力 对比度。因此,它将为以下方面提供可靠的、基于证据的建议 临床医生正在开发新的治疗模式和干预措施 CVR受损。
英文摘要
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.
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Predicting Tissue and Functional Outcome in Acute Stroke
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    10568740
  • 项目类别:
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    $62.82万
  • 财政年份:
    2023
  • 负责人:
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AI-Enhanced Brain PET Imaging for Alzheimer's Disease
  • 批准号:
    10670483
  • 项目类别:
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  • 财政年份:
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  • 负责人:
    Gregory George Zaharchuk
  • 依托单位:
Next Generation Brain PET Imaging
  • 批准号:
    10279862
  • 项目类别:
  • 资助金额:
    $56.46万
  • 财政年份:
    2021
  • 负责人:
    Gregory George Zaharchuk
  • 依托单位:
Next Generation Brain PET Imaging
  • 批准号:
    10478939
  • 项目类别:
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    $54.48万
  • 财政年份:
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  • 负责人:
    Gregory George Zaharchuk
  • 依托单位:
海外基金