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中文摘要
翻译
描述(申请人提供)本建议的目标是开发临床上可行的策略,从动态计算机断层扫描(CT)成像中定量估计心肌血流量(MBF),其低辐射剂量可与普通核心肌灌注研究中的方法相媲美。尽管定量MBF(以毫升/克/分钟为单位)的临床价值已被证明,但目前还没有广泛的临床方法可以方便地以绝对单位来测量MBF。动态CT提供了量化血流的可能性,但这些研究提供的辐射剂量阻碍了人们的广泛接受。该方案的具体目标是:1)最佳的心肌血流量估计方法,2)用于MBF估计的低剂量动态CT采集策略,3)无偏数据恢复算法,4)基于空间分辨率的图像重建方法,以降低噪声并利用先验知识抑制噪声。这些目标将通过动态增强CT成像的模拟来开发,并通过患者检查进行评估。我们假设,通过选择采集策略和合理应用降噪策略,低剂量动态CT可以确定心内膜下和心下膜下MBF的准确估计值。这项工作提出了新的低剂量采集和数据/图像增强策略,以实现以毫升/克/分钟为绝对单位的准确定量血流量估计。这些方法将允许大幅减少辐射剂量,这对于患者的安全、用于MBF测量的动态CT的临床应用以及动态CT的其他经证实的应用都是至关重要的。这项工作将把心脏动态CT定位为一种安全、容易和广泛可用的定量MBF评估工具,为血流限制疾病的量化提供有价值的临床信息,最大限度地减少不必要的导尿术,告知治疗选择,并开发新的治疗方法。
英文摘要
DESCRIPTION (provided by applicant) The objective of this proposal is to develop clinically viable strategies for the quantitative estimation of myocardial blood flow (MBF) from dynamic computed tomography (CT) imaging with low radiation doses comparable to those received in common nuclear myocardial perfusion studies. Despite the proven clinical value of quantifying MBF (in ml/g/min), there are no widespread clinical methods to easily measure MBF in absolute units. Dynamic CT offers the potential to quantify flow, but the radiation dose imparted from these studies prohibits widespread acceptance. The specific aims of the proposal are to develop 1) optimal myocardial blood flow estimation methods, 2) low-dose dynamic CT acquisition strategies for MBF estimation, 3) unbiased data restoration algorithms and 4) image reconstruction methods based on trading off spatial resolution for noise reduction and constraining noise with a priori knowledge. These aims will be developed with simulations of dynamic contrast enhanced CT imaging and evaluated with patient exams. We hypothesize that accurate subendo- and subepi-cardial MBF estimates can be determined with low- dose dynamic CT through selection of acquisition strategies and judicious application of noise reduction strategies. This work proposes novel low-dose acquisition and data/image enhancement strategies to enable accurate quantitative estimates of blood flow in absolute units of ml/g/min. These methods will allow for substantial reductions in radiation dose, which is essential for patient safety, clinical application of dynamic CT for MBF measurement, and for other proven applications of dynamic CT. This work will position cardiac dynamic CT as a safe, easy, and widely available tool for quantitative MBF estimation, providing valuable clinical information for quantification of flow limiting disease, minimizing unnecessary catheterization procedures, informing therapy choices, and developing new therapies.
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Development of Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses
  • 批准号:
    10428946
  • 项目类别:
  • 资助金额:
    $21.7万
  • 财政年份:
    2022
  • 负责人:
    Adam M Alessio
  • 依托单位:
Development of Artificial Intelligence (AI) based algorithms to classify the Pneumoconioses
  • 批准号:
    10709621
  • 项目类别:
  • 资助金额:
    $20.16万
  • 财政年份:
    2022
  • 负责人:
    Adam M Alessio
  • 依托单位:
Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma
  • 批准号:
    9976563
  • 项目类别:
  • 资助金额:
    $17.81万
  • 财政年份:
    2019
  • 负责人:
    Adam M Alessio
  • 依托单位:
IEEE Medical Imaging Conference
  • 批准号:
    8910150
  • 项目类别:
  • 资助金额:
    $1.0万
  • 财政年份:
    2015
  • 负责人:
    Adam M Alessio
  • 依托单位:
海外基金