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中文摘要
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 描述(由申请人提供):该应用的广泛、长期目标是改善颈动脉粥样硬化患者的预后。我们将开发和验证一种快速且非造影剂增强的3D MRI技术,以识别高概率导致中风或短暂性脑缺血发作等神经血管事件的颈动脉粥样硬化斑块。高危颈动脉斑块的主要特征包括斑块内出血(IPH)、钙化(CA)、大的富脂坏死核(LRNC)和薄纤维帽(FC)。MRI被广泛用于基于多对比加权图像(T1、T2加权图像和飞行时间成像)和造影剂增强成像上的组织信号模式来表征颈动脉斑块。然而,传统的协议具有许多主要限制,包括:(1)2D技术的穿透平面分辨率相对较低(2-3 mm),由于部分体积效应,潜在地降低了斑块表征的准确性,2)相对较长的总图像采集时间(>20分钟),3)数据采集过程中吞咽造成的图像伪影,4)使用单独的T1、T2、质子密度加权、飞行时间和对比剂增强图像来表征复杂斑块,导致由于不同采集之间的图像配准错误而导致的潜在误差,最重要的是,5)对于LRNC表征需要基于Gd的对比剂增强成像。因此,肾功能不全的患者将不能接受这种检查,因为有发生肾源性系统性纤维化的风险。因此,对于肾功能不全的患者来说,不使用基于Gd的造影剂的MRI技术可能是至关重要的,因为他们的心血管疾病患病率也很高。该应用程序的目标是开发一种新的MRI技术,并测试这样一种假设,即与传统方案相比,它将数据采集时间减少了3倍,同时不需要造影剂。具体目标是:目标1:开发非造影剂增强的多对比加权动脉粥样硬化表征(NCE-Match)技术目标2:使用组织病理学验证NCE-Match检测到的高危颈动脉斑块特征目标3:验证NCE-Match可以在降脂治疗期间检测到动脉粥样硬化性脂质消耗如果开发成功,该技术将能够识别未来容易发生神经血管事件的患者,从而可以开始改变生活方式或进行药物治疗以降低风险。它还将为评估降脂治疗的反应提供一种衡量标准,这预示着风险的降低。与传统方案相比,NCE-Match将允许对肾功能不全患者进行成像,因为它能够避免使用造影剂。由于更短的成像时间和非造影剂增强成像,它还具有相当大的成本节约潜力。
英文摘要
 DESCRIPTION (provided by applicant): The broad, long-term objective of the application is to improve the prognosis of patients with carotid atherosclerosis. We will develop and validate a fast and non-contrast-agent-enhanced 3D MRI technique to identify carotid atherosclerotic plaques that have a high probability of causing neurovascular events such as stroke or transient ischemic attack. Major features of high-risk carotid plaques include the presence of intra- plaque hemorrhage (IPH), calcification (CA), a large lipid-rich necrotic core (LRNC), and thin fibrous cap (FC). MRI is used extensively to characterize carotid artery plaques based on tissue signal patterns on multi- contrast-weighted images (T1-, T2-weighted imaging and time-of-flight) and contrast-agent-enhanced imaging. However, the conventional protocol has a number of major limitations, including: (1) Relatively low through- plane resolution (2-3 mm) with 2D techniques, potentially reducing the accuracy of plaque characterization due to the partial volume effect, 2) relatively long total image acquisition time (> 20 min), 3) image artifacts caused by swallowing during data acquisition, 4) complex plaque characterization using separate T1-, T2-, proton- density-weighted, time-of-flight, and contrast-agent-enhanced images, resulting in potential errors due to image misregistration between separate acquisitions, and most importantly, 5) the requirement of gadolinium-based- contrast-agent-enhanced imaging for LRNC characterization. As a result, patients with renal insufficiency will not be able to undergo this examination due to the risk of developing nephrogenic systemic fibrosis. Therefore, MRI techniques without gadolinium-based contrast media could be vitally important for patients with renal insufficiency, who also have high prevalence for cardiovascular disease. The goal of the application is to develop a new MRI technique and test the hypothesis that it reduces data acquisition time by a factor of 3 while without the need for contrast media as compared to the conventional protocol. Specific aims are: Aim 1: To develop the non-contrast-agent-enhanced, multi-contrast-weighted atherosclerosis characterization (NCE-MATCH) technique Aim 2: To validate high-risk carotid plaque features detected by NCE-MATCH using histopathology Aim 3: To verify that NCE-MATCH can detect atherosclerotic lipid depletion during lipid lowering therapy If successfully developed, the technique will be able to identify patients who are prone to future neurovascular events so life style changes or medical therapies can be initiated to reduce the risks. It will als provide a measure for evaluating the response to lipid lowering therapy, which is predictive of reduced risk. Compared to the conventional protocol, NCE-MATCH will allow imaging of patients with renal insufficiency due to its ability to avoid contrast agents. It also has the potential for considerable cost savings due to shorter imaging time and non-contrast-agent-enhanced imaging.
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Predicting Pancreatic Ductal Adenocarcinoma (PDAC) Through Artificial Intelligence Analysis of Pre-Diagnostic CT Images
  • 批准号:
    10475648
  • 项目类别:
  • 资助金额:
    $100.05万
  • 财政年份:
    2021
  • 负责人:
    Debiao Li
  • 依托单位:
Predicting Pancreatic Ductal Adenocarcinoma (PDAC) Through Artificial Intelligence Analysis of Pre-Diagnostic CT Images
  • 批准号:
    10693185
  • 项目类别:
  • 资助金额:
    $93.44万
  • 财政年份:
    2021
  • 负责人:
    Debiao Li
  • 依托单位:
An Accurate Non-Contrast-Enhanced Cardiac MRI Method for Imaging Chronic Myocardial Infarctions: Technical Developments to Rapid Clinical Validation
  • 批准号:
    9899302
  • 项目类别:
  • 资助金额:
    $86.85万
  • 财政年份:
    2017
  • 负责人:
    Debiao Li
  • 依托单位:
4Dx Small Animal Scanner for Functional Lung Imaging
  • 批准号:
    9075865
  • 项目类别:
  • 资助金额:
    $60.0万
  • 财政年份:
    2016
  • 负责人:
    Debiao Li
  • 依托单位:
海外基金