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New models and contrast agents for dynamic contrast-enhanced MRI.

机译:用于动态对比增强MRI的新模型和对比剂。

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摘要

Angiogenesis is a fundamental driver of tumor biology and many other important aspect of human health. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) has been shown to be a valuable biomarker for the indirect assessment of angiogenesis. However, DCE-MRI is very specialized technique that has limitations. In this dissertation new models and contrast agents to address some of these limitations are presented. Chapter 1 presents an introduction to DCE-MRI, the rationale to asses tumor biology with this technique, the MRI pulses sequences and the standard pharmacokinetic modeling used for the analysis of DCE-MRI data. Chapter 2 describes the application of DCE-MRI to asses the response to the hypoxia-activated drug TH-302. It is shown that DCE-MRI can detect a response after only 24 hours of initiating therapy. In Chapter 3, a new model for the analysis of DCE-MRI is presented, the so-called Linear Reference Region Model (LRRM). This new model improves upon existing models and it was demonstrated that it is ∼620 faster than current algorithms and 5 times less sensitive to noise, and more importantly less sensitive to temporal resolution which enables the analysis of DCE-MRI data obtained in the clinical setting, which opens a new area of study in clinical MRI. Chapter 4 describes the extension of the LRRM to estimate the absolute permeability of two fluorinated contrast agents; we call this approach the Reference Agent Model (RAM). In order to make this new model an experimental reality, a novel pulse sequence and contrast agents (CA) for 19F MRI were developed. Two contributions to the field of DCE-MRI are presented in this chapter, the first simultaneous 19F-DCE-MRI detection of two fluorinated CA in a mouse model of breast cancer, and the estimation of their relative permeability. RAM eliminates some of the physiological variables that affect DCE-MRI, which may improve its sensitivity and specificity. Finally, new potential applications of LRRM and RAM are discussed in Chapter 5.
机译:血管生成是肿瘤生物学和人类健康许多其他重要方面的基本驱动力。动态对比增强磁共振成像(DCE-MRI)已被证明是间接评估血管生成的有价值的生物标记。然而,DCE-MRI是非常专业的技术,具有局限性。本文提出了解决这些局限性的新模型和造影剂。第1章介绍了DCE-MRI,使用该技术评估肿瘤生物学的原理,MRI脉冲序列以及用于DCE-MRI数据分析的标准药代动力学模型。第2章介绍了DCE-MRI在评估对缺氧激活药物TH-302的反应中的应用。结果表明DCE-MRI仅在开始治疗24小时后即可检测到反应。在第3章中,提出了一种用于DCE-MRI分析的新模型,即所谓的线性参考区域模型(LRRM)。这个新模型对现有模型进行了改进,并证明它比现有算法快620左右,对噪声的敏感性低5倍,更重要的是对时间分辨率的敏感性较低,这使得能够分析在临床环境中获得的DCE-MRI数据,这为临床MRI开辟了一个新的研究领域。第4章介绍了LRRM的扩展,以估计两种氟化造影剂的绝对渗透率。我们将此方法称为参考代理模型(RAM)。为了使该新模型成为实验现实,开发了一种用于19F MRI的新型脉冲序列和造影剂(CA)。本章介绍了对DCE-MRI领域的两个贡献,这是首次在乳腺癌小鼠模型中同时19F-DCE-MRI检测两个氟化CA,并估算了它们的相对渗透性。 RAM消除了一些影响DCE-MRI的生理变量,这可能会提高其敏感性和特异性。最后,第5章讨论了LRRM和RAM的新潜在应用。

著录项

  • 作者单位

    The University of Arizona.;

  • 授予单位 The University of Arizona.;
  • 学科 Chemistry Biochemistry.;Health Sciences Oncology.;Engineering Biomedical.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 111 p.
  • 总页数 111
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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