Quantitative Susceptibility Mapping for Stroke Risk Prediction of Vulnerable Carotid Plaques

用于预测易损颈动脉斑块中风风险的定量敏感性图

基本信息

  • 批准号:
    10609912
  • 负责人:
  • 金额:
    $ 68.9万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-04-15 至 2027-03-31
  • 项目状态:
    未结题

项目摘要

Our main objective is to use quantitative susceptibility mapping (QSM) in establishing reliable noninvasive MRI for identification and risk stratification of unstable carotid atherosclerotic plaques. Currently, decisions about carotid revascularization to prevent stroke, such as carotid endarterectomy or carotid artery stenting, are based on whether there is ?50% carotid artery stenosis. However, this strategy uses only one feature of vulnerable plaque and frequently misclassifies patients. Using imaging to identify other features of rupture-prone carotid plaques with high risk for thromboembolic stroke, in combination with stenosis assessment, proves to be a more effective approach for risk evaluation. Of these features, intraplaque hemorrhage (IPH) is associated with a 4 to 6-fold higher risk of stroke, while calcification is associated with a 50% lower stroke risk. In the conventional approach, IPH and calcification are defined as hyperintensity and hypointensity, respectively, in a plaque region on the T1-weighted (T1w) image acquired as part of the multi-contrast MRI (mcMRI) protocol. However, T1w hyperintensity only captures the transient methemoglobin phase of hemorrhage. In the ensuing hemosiderin phase, IPH appears hypointense due to the strong susceptibility-induced dephasing effects of the superparamagnetic hemosiderin (susceptibility>150 ppm), which can be misinterpreted as calcification, although calcification is strongly diamagnetic (-2.3 ppm). The key scientific premise of this proposal is that QSM can reliably resolve T1w hypointensity into IPH hemosiderin versus calcification based on their different magnetic property, and therefore will significantly improve imaging characterization and risk stratification of patients with atherosclerotic carotid plaques. We have pioneered QSM development and demonstrated the exquisite sensitivity of QSM for hemorrhage and calcification in carotid plaque. In this project, we will further improve the utility of carotid plaque QSM for routine clinical imaging by developing a multi-contrast QSM (mcQSM) approach which can provide not only QSM but also traditional mcMRI contrasts in 5 min scan time. We will develop a nonlinear QSM reconstruction algorithm which is robust against noise and motion and can separate co-existing IPH and calcification to improve IPH detection in calcified vessels. We will then establish the improvement in diagnostic accuracy of mcQSM over mcMRI for detecting IPH and calcification in patients who are scheduled for carotid endarterectomy. Finally, we will test the hypothesis that mcQSM will provide significantly higher discrimination for stroke than mcMRI. A successful outcome of this proposal will make carotid plaque QSM ready for widespread and routine clinical use in the emerging era of personalized medicine to reduce the individual and societal burden of stroke.
我们的主要目标是使用定量易感地形图(QSM)建立可靠的无创性MRI 用于不稳定颈动脉粥样硬化斑块的识别和风险分层。目前,关于以下方面的决定 颈动脉血管重建术预防中风,如颈动脉内膜切除术或颈动脉支架植入术,是基于 关于是否有?50%的颈动脉狭窄。然而,该策略仅使用了VULNERABLE的一个功能 斑块并经常错误地对患者进行分类。利用影像识别易破裂颈动脉的其他特征 血栓栓塞性中风的高风险斑块,结合狭窄评估,被证明是一种 更有效的风险评估方法。在这些特征中,斑块内出血(IPH)与 中风的风险增加4到6倍,而钙化与中风风险降低50%相关。在 传统的方法,IPH和钙化分别被定义为高信号和低信号。 作为多对比磁共振成像(McMRI)协议一部分采集的T1加权(T1w)图像上的斑块区域。 然而,T1w高信号仅捕捉到出血的一过性高铁血红蛋白阶段。在接下来的几天里 在含铁血黄素阶段,IPH表现为低信号,这是由于强的易感性诱导的消融作用 超顺磁性含铁血黄素(磁化率和150ppm),可能被误解为钙化, 尽管钙化具有很强的抗磁性(-2.3ppm)。这一提议的关键科学前提是 QSM可以可靠地将T1w低信号分解为IPH含铁血黄素和钙化,基于它们的不同 磁性,因此将显著改善成像特征和风险分层 颈动脉粥样硬化性斑块患者。我们率先开发了QSM,并展示了 QSM对颈动脉斑块内出血和钙化具有极高的敏感性。在这个项目中,我们将进一步 开发多对比QSM提高颈动脉斑块QSM在临床常规成像中的应用 (McQSM)方法既能提供QSM,又能提供传统mcMRI的对比度,扫描时间为5min。 我们将开发一种非线性QSM重建算法,该算法对噪声和运动具有鲁棒性,并且可以 分离共存的IPH和钙化,以提高钙化血管中IPH的检测。然后我们将确定 McQSM对IPH和钙化诊断准确率的提高 他们被安排做颈动脉内膜切除术。最后,我们将测试mcQSM将提供的假设 对中风的识别率明显高于mcMRI。这项提议的成功结果将使 在个性化的新兴时代,颈动脉斑块QSM准备广泛和常规的临床使用 减轻中风的个人和社会负担的医学。

项目成果

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Ajay Gupta其他文献

Ajay Gupta的其他文献

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{{ truncateString('Ajay Gupta', 18)}}的其他基金

Development of a dry powder inhalation product against Respiratory Syncytial Virus based on an endogenous anionic pulmonary surfactant lipid
基于内源性阴离子肺表面活性剂脂质的抗呼吸道合胞病毒干粉吸入产品的开发
  • 批准号:
    10697027
  • 财政年份:
    2023
  • 资助金额:
    $ 68.9万
  • 项目类别:
Quantitative susceptibility mapping for stroke risk prediction of vulnerable carotid plaques
用于预测易损颈动脉斑块中风风险的定量敏感性图
  • 批准号:
    10446087
  • 财政年份:
    2022
  • 资助金额:
    $ 68.9万
  • 项目类别:
Understanding the dynamic interactions between tau pathology and microgliamediated inflammation in Alzheimer's Disease
了解阿尔茨海默病中 tau 蛋白病理学与小胶质细胞介导的炎症之间的动态相互作用
  • 批准号:
    10622513
  • 财政年份:
    2021
  • 资助金额:
    $ 68.9万
  • 项目类别:
Understanding the dynamic interactions between tau pathology and microgliamediated inflammation in Alzheimer's Disease
了解阿尔茨海默病中 tau 蛋白病理学与小胶质细胞介导的炎症之间的动态相互作用
  • 批准号:
    10317631
  • 财政年份:
    2021
  • 资助金额:
    $ 68.9万
  • 项目类别:
Understanding the dynamic interactions between tau pathology and microgliamediated inflammation in Alzheimer's Disease
了解阿尔茨海默病中 tau 蛋白病理学与小胶质细胞介导的炎症之间的动态相互作用
  • 批准号:
    10471976
  • 财政年份:
    2021
  • 资助金额:
    $ 68.9万
  • 项目类别:
MRI Detection of CarotId Plaques as a mecHanism for Embolic strokes of undeteRmined source (MRI DECIPHER)
颈动脉斑块的 MRI 检测作为不明原因栓塞性中风的机制(MRI DECIPHER)
  • 批准号:
    10204095
  • 财政年份:
    2019
  • 资助金额:
    $ 68.9万
  • 项目类别:
A Machine Learning Approach For CTA-based Plaque Characterization and Stroke Risk Prediction in Carotid Artery Atherosclerosis
基于 CTA 的颈动脉粥样硬化斑块表征和中风风险预测的机器学习方法
  • 批准号:
    9904175
  • 财政年份:
    2019
  • 资助金额:
    $ 68.9万
  • 项目类别:
MRI Detection of CarotId Plaques as a mecHanism for Embolic strokes of undeteRmined source (MRI DECIPHER)
颈动脉斑块的 MRI 检测作为不明原因栓塞性中风的机制(MRI DECIPHER)
  • 批准号:
    10661676
  • 财政年份:
    2019
  • 资助金额:
    $ 68.9万
  • 项目类别:
MRI Detection of CarotId Plaques as a mecHanism for Embolic strokes of undeteRmined source (MRI DECIPHER)
颈动脉斑块的 MRI 检测作为不明原因栓塞性中风的机制(MRI DECIPHER)
  • 批准号:
    10449116
  • 财政年份:
    2019
  • 资助金额:
    $ 68.9万
  • 项目类别:
Parallel Algorithms for Big Data from Mass Spectrometry based Proteomics
基于质谱的蛋白质组学大数据并行算法
  • 批准号:
    9301702
  • 财政年份:
    2017
  • 资助金额:
    $ 68.9万
  • 项目类别:

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