| Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful noninvasive tool for the visualization and measurement of myocardial blood flow and the myocardial distribution volume (Ve). A quantitative estimate of the magnitude and spatial distribution of myocardial blood flow and Ve may allow for the early detection of perfusion deficits and ischemia, which are often associated with various types of coronary artery disease (CAD). Accurate estimates of blood flow and Ve may also provide a means for clinicians to track changes in myocardial injury and provide more effective patient care. The primary goal of this research is to develop image analysis tools and methods to noninvasively and accurately quantify myocardial blood flow and Ve from DCE-MRI perfusion studies, in order to improve the detection and characterization of CAD and thus provide effective patient treatment and care.;Second, four quantitative analysis methods (2-compartment modeling, Fermi function modeling, model-independent analysis, and Patlak plot analysis) used to estimate myocardial blood flow are implemented with DCE-MRI data acquired in 20 human subjects. Aggregate rest perfusion estimates were not significantly different between all four analysis methods. At stress, perfusion estimates were not significantly different between 2-compartment modeling, model-independent analysis, and Patlak plot analysis. Myocardial perfusion reserve values were not significantly different between all four methods. Model-independent analysis resulted in the lowest model fit errors. When more than just the first-pass of data was analyzed, perfusion estimates from 2-compartment modeling and model-independent analysis did not change significantly, unlike results from Fermi function modeling.;Finally, a technique for estimating Ve, using 2-compartment kinetic modeling, is developed. This analysis method requires less time than an existing steady-state MRI measurement method and results in estimates of Ve in normal and scarred myocardium that are comparable to results from steady-state MRI and histology studies.;First, a fully model-independent deconvolution analysis method that uses iterative minimization and temporal regularization is developed and evaluated for estimating myocardial blood flow from DCE-MRI perfusion studies. Blood flow estimates in five human subjects analyzed with this method are shown to correlate well (r=0.85) with blood flow estimates from dynamic 13N-ammonia positron emission tomography (PET), a current gold standard for noninvasively estimating myocardial blood flow. |
