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Multi-slice Perfusion MR Imaging of the Human Kidney

Multi-slice Perfusion MR Imaging of the Human Kidney
人体肾脏的多层灌注 MR 成像
批准号:
6693779
负责人:
H MICHAEL GACH
金额:
$14.9万
依托单位国家:
美国
项目类别:
财政年份:
2003
资助国家:
美国
项目状态:
已结题
起止时间:
2003-01-01 至 2005-12-31

项目摘要

项目成果

H MICHAEL GACH的其他基金

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中文摘要
翻译
描述(申请人提供):肾移植是治疗终末期肾病最具成本效益的方法。然而,急性排斥反应(AR)、移植物功能延迟(DGF)和免疫抑制药物肾毒性仍然是常见的并发症。区分AR与DGF和免疫抑制药物毒性是很重要的,因为每种药物的治疗选择可能有很大不同。生物测定血清肌酐、血尿素氮(BUN)和尿量可提示肾功能不全的发生,但不能提示类型。活组织检查仍然是金标准,但它是侵入性的和高度局限的。肾灌注是排斥反应的一个敏感和有区别的参数。AR的特征是肾脏灌注和功能明显突然减少,而在定性MRI研究中,DGF和免疫抑制药物毒性通常显示灌注正常或轻微减少。灌注MRI可以识别肾脏的空间和时间灌注变化,这将有助于AR的诊断。我们研究的具体目的是:1)发展和改进无创多层ASL灌注MRI,使用近端和远端同时照射(SPDI), 2)使用SPDI测量健康和移植志愿者的灌注率。SPDI使用连续动脉自旋标记(CASL)的一种变体来标记内源性动脉水。将使用超快速快照技术(例如EPI)获取MR图像。本研究旨在测试SPDI检测区域灌注变化的灵敏度和可靠性,并将SPDI与定量多层动态敏感性对比(DSC)测量结果进行比较。灌注MRI技术将在移植后的头三个月内在健康和移植的肾脏中进行两次测试。移植肾脏的状况将根据生物测定和活检结果进行分类(例如,正常、AR和DGF)。本研究将验证以下假设:1)使用SPDI的多层MRI可以可靠、准确地测量人体肾皮质灌注率,系统误差小于0.6 ml/g-rain; 2)移植后3个月内大于1ml /g-min的肾皮质灌注率变化,可与MR血管造影、生化和活检数据相结合,用于区分AR与DGF和免疫抑制药物毒性。
英文摘要
DESCRIPTION (provided by applicant): Kidney transplantation is the most cost-effective therapy for end-stage renal disease. However, acute rejection (AR), delayed graft function (DGF) and immunosuppressive drug nephrotoxicity remain common complications. It is important to distinguish AR from DGF and immunosuppressive drug toxicity, since the therapeutic options for each may differ significantly. Bioassays of serum creatinine, blood urea nitrogen (BUN), and urine output may indicate the occurrence of renal dysfunction but not the type. Biopsy remains the gold standard, but is invasive and highly localized. Renal perfusion can be a sensitive and distinguishing parameter of the rejection process. AR is characterized by a marked, sudden decrease in both perfusion and function of the kidney while DGF and immunosuppressive drug toxicity typically show normal or slightly reduced perfusion in qualitative MRI studies. Perfusion MRI can identify spatial and temporal perfusion changes in the kidney that will aid in the diagnosis of AR. The specific aims of our research are: 1) to develop and improve non-invasive multi-slice ASL perfusion MRI using simultaneous proximal and distal irradiation (SPDI) and 2) to use SPDI to measure perfusion rates in healthy and transplant volunteers. SPDI labels endogenous arterial water using a variant of continuous arterial spin labeling (CASL). MR images will be acquired with ultra-fast snapshot techniques (e.g., EPI). The research is designed to test the sensitivity and reliability of SPDI for detecting regional perfusion variations, and compare SPDI with quantitative multi-slice dynamic susceptibility contrast (DSC) measurements. The perfusion MRI technique will be tested in healthy and transplanted kidneys twice during the first three months following transplantation. The condition of the transplanted kidneys will be categorized (e.g., normal, AR, and DGF) based on bioassay and biopsy results. The study will test the hypotheses that 1) multi-slice MRI using SPDI can reliably and accurately measure renal cortical perfusion rates in humans with a systematic error of less than 0.6 ml/g-rain, and 2) changes in renal cortical perfusion rates of greater than 1 ml/g-min in the period within 3 months after transplantation can be used in association with MR angiographic, biochemical, and biopsy data to distinguish AR from DGF and immunosuppressive drug toxicity.
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Novel imaging and treatment technologies for image-guided noninvasive stereotactic cardiac radiosurgery
  • 批准号:
    9803019
  • 项目类别:
  • 资助金额:
    $46.68万
  • 财政年份:
    2019
  • 负责人:
    H MICHAEL GACH
  • 依托单位:
Novel imaging and treatment technologies for image-guided noninvasive stereotactic cardiac radiosurgery
  • 批准号:
    10227686
  • 项目类别:
  • 资助金额:
    $42.05万
  • 财政年份:
    2019
  • 负责人:
    H MICHAEL GACH
  • 依托单位:
Novel imaging and treatment technologies for image-guided noninvasive stereotactic cardiac radiosurgery
  • 批准号:
    10463571
  • 项目类别:
  • 资助金额:
    $41.45万
  • 财政年份:
    2019
  • 负责人:
    H MICHAEL GACH
  • 依托单位:
Free-Breathing Perfusion MRI of the Abdomen with Predictive Motion Correction