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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. PUBLIC HEALTH RELEVANCE: A quantitative estimate of blood flow in the heart provides valuable clinical information regarding the severity and extent of coronary heart disease. Dynamic computed tomography (CT) offers the potential to quantify blood flow, but it is not widely accepted because of the radiation dose associated with these studies. This proposal will develop strategies to substantially reduce the radiation dose from dynamic CT, enabling quantitative myocardial blood flow estimation from an easy, widely available, and safe CT imaging study.
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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:
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:
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:
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