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 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