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英文摘要
Symptomatic urinary stone disease affects approximately 900,000 persons in the United States each year, resulting in an estimated annual medical cost of $4.5 billion. Computed tomography (CT) is the established method for imaging urinary calculi and can provide accurate sub-millimeter details of the size and location of renal stones. However, a complete characterization of renal stones, which includes stone composition and fragility, is critically needed for safe and cost effective management of stone disease, as well as for phenotyping of research subjects. Our long term goal is to use advanced CT methodologies to fully characterize urinary calculi, and to use evolving technology to reliably detect stone precursor lesions. Our objectives in this application are to develop a comprehensive, low-dose, stone-characterization exam using commercially-available dual-energy CT technology, and to detect stone precursor lesions using a prototype CT system equipped with energy resolving detectors. Based on our extensive preliminary results, we know that dual-energy CT can discriminate between several types of renal stones and provide accurate quantification of stone morphology. Our central hypothesis is that this quantitative information can be acquired at reduced dose levels and used to predict stone fragility, which we define as the likelihood of a stone to be broken by SWL or endoscopic methods. We further hypothesize that use of pre-clinical, 225-micron, energy-resolving CT detectors will allow detection of stone precursor lesions such as Randall's plaques, as well as the presence of trace elements having a suspected role in stone formation, such as zinc. We will accomplish these objectives through two specific aims: 1) Aim 1: Develop and validate a comprehensive low-dose stone-characterization exam using clinical dual-energy CT techniques. 2) Aim 2: Develop a pre-clinical spectral CT imaging technique that can detect precursor lesions and trace elements related to the formation of kidney stones.
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Quantitative, non-invasive characterization of urinary stone composition and fragility using multi-energy CT and machine learning techniques
  • 批准号:
    10377461
  • 项目类别:
  • 资助金额:
    $35.75万
  • 财政年份:
    2019
  • 负责人:
    Cynthia H McCollough
  • 依托单位:
Trade-offs in human observer performance, image quality metrics, and patient dose
  • 批准号:
    9901529
  • 项目类别:
  • 资助金额:
    $55.48万
  • 财政年份:
    2019
  • 负责人:
    Cynthia H McCollough
  • 依托单位:
Trade-offs in human observer performance, image quality metrics, and patient dose
  • 批准号:
    10322422
  • 项目类别:
  • 资助金额:
    $55.32万
  • 财政年份:
    2019
  • 负责人:
    Cynthia H McCollough
  • 依托单位:
Critical resources to evaluate CT scan techniques and dose reduction approaches
  • 批准号:
    9261249
  • 项目类别:
  • 资助金额:
    $10.2万
  • 财政年份:
    2016
  • 负责人:
    Cynthia H McCollough
  • 依托单位:
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