Reliable Evaluation of Coronary Artery Disease using Myocardial BOLD MRI with CO2

使用 CO2 心肌 BOLD MRI 可靠评估冠状动脉疾病

基本信息

  • 批准号:
    8725722
  • 负责人:
  • 金额:
    $ 40.82万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2008
  • 资助国家:
    美国
  • 起止时间:
    2008-04-15 至 2017-05-31
  • 项目状态:
    已结题

项目摘要

DESCRIPTION (provided by applicant): Coronary artery disease is the leading cause of death in the United States. It is estimated that in excess of 16 million people are living with coronary artery disease (CAD) and more than 1 in three deaths are due to CAD in the US. In addition, more than 1 million people are hospitalized each year in the US because of CAD. The most common form of CAD leads to the narrowing of the coronary arteries (stenosis) resulting in reduced blood flow and oxygen supplied to the heart muscle, results in myocardial ischemia - a condition where the oxygen demand of the myocardium is far in excess of the available supply. The presence of myocardial ischemia is an important risk factor for major adverse cardiac events (MACE: death, myocardial infarction, and stroke). Appropriate early interventions (coronary bypass, angioplasty, pharmacological, or medical therapy) that are guided by the extent and severity of ischemic burden associated with stable CAD may be instrumental in reducing the risk of MACE. Hence a reliable, non-invasive, and repeatable method for determining the extent and severity of ischemia is invaluable in managing patients with CAD. The broad, long-term objective of this proposal is to improve the prognosis of patients with coronary artery disease. An ideal strategy for assessing clinically significant myocardial ischemia would be completely non-invasive; that is, in addition to avoiding the risks of cardiac catheterization, it would be free of ionizing radiation, exogenous contrast media, and pharmacological stress-agents with both risks and side effects. Although the current clinical standards do not provide many of these benefits, a number of these requirements can be met by myocardial Blood-Oxygen-Level-Dependent (BOLD) MRI. However, the widespread use of this approach is currently limited by inadequate sensitivity, specificity, and the need for intravenous pharmacological (adenosine) stress, all of which pose considerable limitations in clinical practice. In the proposed project, we wish to significantly improve the reliability of myocardial BOLD MRI so that is becomes powerful enough to accurately quantify the ischemic volume associated with clinically significant CAD without intravenous pharmacological stress or exogenous contrast media. The proposed method utilizes (i) an individualized targeted change in arterial partial pressure of CO2 (PaCO2) as the non-invasive vasoactive stimulus, (ii) fast, high-resolution, 4D BOLD MRI at 3T and (iii) the generalized linear model (GLM) theory to derive statistical parametric maps (SPM) to reliably detect and quantify the clinically significant ischemic myocardial volume in a data-driven fashion. These studies are expected to provide the fundamental scientific basis for a non-invasive and reliable imaging strategy for evaluating prognosis of patients with coronary artery disease.
描述(由申请人提供):冠状动脉疾病是美国死亡的主要原因。据估计,超过1600万人患有冠状动脉疾病(CAD),三分之一的死亡人数归因于美国的CAD。此外,由于CAD,在美国,每年有超过100万人住院。 CAD的最常见形式导致冠状动脉(狭窄)的狭窄导致血液流量减少和供应给心脏肌肉的氧气,导致心肌缺血 - 这种状况,在这种情况下,心肌的氧气需求远远超出了可用的供应。心肌缺血的存在是重大不良心脏事件的重要危险因素(MACE:死亡,心肌梗塞和中风)。根据与稳定CAD相关的缺血负担的程度和严重程度指导的适当的早期干预措施(冠状动脉搭桥,血管成形术,药理学或药物治疗)可能对降低MACE的风险有用。因此,一种可靠,无创和可重复的方法来确定缺血的程度和严重性对于管理CAD患者而言是无价的。 该提案的广泛长期目标是改善冠状动脉疾病患者的预后。评估临床上重要的心肌缺血的理想策略是完全无创的。也就是说,除了避免心脏导管插入术的风险外,它还没有电离辐射,外源性对比培养基和具有风险和副作用的药理学压力。尽管当前的临床标准并未提供许多此类益处,但是心肌血氧级依赖性(BOLD)MRI可以满足许多这些要求。但是,这种方法的广泛使用目前受到敏感性不足,特异性以及对静脉药理学(腺苷)胁迫的需求的限制,所有这些应激在临床实践中都构成了相当大的限制。 在拟议的项目中,我们希望显着提高心肌BOLD MRI的可靠性,使其变得足够强大,可以准确地量化与临床上显着的CAD相关的缺血体积,而无需静脉内药理应激或外源性对比培养基。所提出的方法利用(i)二氧化碳(PACO2)作为非侵入性血管活性刺激的个性化靶向变化,(ii)快速,高分辨率,4D BOLD MRI在3T和(iii)(iii)(iii)广义线性模型(GLM)理论,以得出统一的统一参数图(Spmaps),并量身定量量身定量(spm),并量身定量量化。 以数据驱动的方式进行缺血性心肌体积。这些研究有望为评估冠状动脉疾病患者预后的非侵入性和可靠的成像策略提供基本科学基础。

项目成果

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Rohan Dharmakumar其他文献

Rohan Dharmakumar的其他文献

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{{ truncateString('Rohan Dharmakumar', 18)}}的其他基金

Mechanistic Insights to A Translatable Therapy for Acute Reperfused Hemorrhagic Myocardial Infarctions
急性再灌注出血性心肌梗塞可转化疗法的机制见解
  • 批准号:
    10359807
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Accurate, Needle-Free, MRI-based Detection of Ischemic Heart Disease without Contrast Agents
无需造影剂即可通过 MRI 进行准确、无针、基于 MRI 的缺血性心脏病检测
  • 批准号:
    10686342
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Accurate, Needle-Free, MRI-based Detection of Ischemic Heart Disease without Contrast Agents
无需造影剂即可通过 MRI 进行准确、无针、基于 MRI 的缺血性心脏病检测
  • 批准号:
    9981378
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Mechanistic Insights to A Translatable Therapy for Acute Reperfused Hemorrhagic Myocardial Infarctions
急性再灌注出血性心肌梗塞可转化疗法的机制见解
  • 批准号:
    10630055
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Mechanistic Insights to A Translatable Therapy for Acute Reperfused Hemorrhagic Myocardial Infarctions
急性再灌注出血性心肌梗塞可转化疗法的机制见解
  • 批准号:
    9887771
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Accurate, Needle-Free, MRI-based Detection of Ischemic Heart Disease without Contrast Agents
无需造影剂即可通过 MRI 进行准确、无针、基于 MRI 的缺血性心脏病检测
  • 批准号:
    10201743
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Accurate, Needle-Free, MRI-based Detection of Ischemic Heart Disease without Contrast Agents
无需造影剂即可通过 MRI 进行准确、无针、基于 MRI 的缺血性心脏病检测
  • 批准号:
    10655692
  • 财政年份:
    2020
  • 资助金额:
    $ 40.82万
  • 项目类别:
Developing a MRI-guided Disease-Modifying Therapy for Post Infarction Chronic Heart Failure
开发 MRI 引导的梗死后慢性心力衰竭疾病缓解疗法
  • 批准号:
    9981537
  • 财政年份:
    2017
  • 资助金额:
    $ 40.82万
  • 项目类别:
Developing a MRI-guided Disease-Modifying Therapy for Post Infarction Chronic Heart Failure
开发 MRI 引导的梗死后慢性心力衰竭疾病缓解疗法
  • 批准号:
    9756228
  • 财政年份:
    2017
  • 资助金额:
    $ 40.82万
  • 项目类别:
4D SSFP MRI for Detecting Functionally Important Coronary Artery Stenosis at Rest
4D SSFP MRI 用于检测静息时具有重要功能的冠状动脉狭窄
  • 批准号:
    7837299
  • 财政年份:
    2009
  • 资助金额:
    $ 40.82万
  • 项目类别:

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