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中文摘要
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 描述(由申请人提供):本申请的广泛、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和非造影剂增强的3D MRI技术,以识别颈动脉粥样硬化斑块,这些斑块很可能导致神经血管事件,如中风或短暂性脑缺血发作。高危颈动脉斑块的主要特征包括斑块内出血(IPH)、钙化(CA)、大的富脂坏死核心(LRNC)和薄的纤维帽(FC)。MRI广泛用于基于多对比加权图像(T1、T2加权成像和飞行时间)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,常规协议具有许多主要限制,包括:(1)2D技术的通过平面分辨率相对较低(2-3 mm),由于部分容积效应,可能降低斑块表征的准确性,2)总图像采集时间相对较长(> 20分钟),3)在数据采集期间由吞咽引起的图像伪影,4)使用单独的T1-、T2-、质子密度加权、飞行时间的复杂斑块表征,和造影剂增强图像,由于单独采集之间的图像配准不良而导致潜在误差,最重要的是,5)要求钆基造影剂增强成像用于LRNC表征。因此,肾功能不全患者将无法接受这项检查,因为有发生肾源性全身性纤维化的风险。因此,不含钆基造影剂的MRI技术对于肾功能不全患者至关重要,这些患者也有较高的心血管疾病患病率。该应用程序的目标是开发一种新的MRI技术,并测试以下假设:与传统方案相比,该技术将数据采集时间缩短3倍,同时不需要造影剂。具体目标是:目标1:建立非造影剂增强的多对比加权动脉粥样硬化特征(NCE-MATCH)技术目的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的3:为了验证NCE-MATCH是否可以在降脂治疗期间检测动脉粥样硬化脂质耗竭,如果成功开发,该技术将能够识别出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药物治疗以降低风险。它也将提供一种评估对降脂治疗反应的方法,这是降低风险的预测指标。与传统方案相比,NCE-MATCH可避免使用造影剂,因此可对肾功能不全患者进行成像。由于成像时间更短和非造影剂增强成像,它还可以节省大量成本。
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