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New Models and Contrast Agents for Dynamic Contrast-Enhanced MRI

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.

Identiferoai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/222845
Date January 2012
CreatorsCardenas Rodriguez, Julio César
ContributorsPagel, Mark D., Mash, Eugene, Ghosh, Indraneel, Trouard, Theodore, Backer, Amanda F., Pagel, Mark D.
PublisherThe University of Arizona.
Source SetsUniversity of Arizona
LanguageEnglish
Detected LanguageEnglish
Typetext, Electronic Dissertation
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

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