Myocardial perfusion imaging is in wide use clinically. Modeling approaches offer a rich variety of ways to improve or optimize estimates of perfusion. Three applications of modeling to cardiac perfusion will be presented in this talk. First, the most standard application of modeling is to use tracer
kinetic models to fit time curves from dynamic image sets. Depending on
the model and the data, the fits can be related to quantitative
perfusion (units of ml/min/g) and perfusion reserve. Emphasis will be on
techniques using dynamic MRI data, although PET will also be considered. Second, accurate measurement of the arterial input function is usually considered critical for quantitative perfusion measurements. Model-based approaches for estimating perfusion without a measured arterial input function will be discussed. And third, model-based reconstruction methods applied to sparse MRI
perfusion datasets can offer significant
advantages over standard inverse Fourier transform methods. Approaches of this type will be illustrated.
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