We show how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data can constrain a compartmental model for analyzing dynamic positron emission tomography (PET) data. We first develop the theory that enables the use of DCE-MRI data to separate whole tissue time activity curves (TACs) available from dynamic PET data into individual TACs associated with the blood space, the extravascular-extracellular space (EES), and the extravascular-intracellular space (EIS). Then we simulate whole tissue TACs over a range of physiologically relevant kinetic parameter values and show that using appropriate DCE-MRI data can separate the PET TAC into the three components with accuracy that is noise dependent. The simulations show that accurate blood, EES, and EIS TACs can be obtained as evidenced by concordance correlation coefficients >0.9 between the true and estimated TACs. Additionally, provided that the estimated DCE-MRI parameters are within 10% of their true values, the errors in the PET kinetic parameters are within approximately 20% of their true values. The parameters returned by this approach may provide new information on the transport of a tracer in a variety of dynamic PET studies. 1. Introduction There is an extensive literature on the use of compartmental modeling to understand the distribution and retention of various positron emission tomography (PET) radiotracers (see, e.g., [1, 2]). A series of ordinary, first-order, linear differential equations are often used to model the body as a series of well-mixed “compartments,” between which a tracer may be transported. Solving the differential equations and then fitting those solutions to measured tissue time activity curves (TACs) return estimates of a number of relevant physiological parameters. Typical dynamic PET models return parameters describing the metabolic rates of tracer utilization. The models used to extract these parameters have several free parameters and the measured TAC is, in practice, a weighted sum of unknown TACs from multiple compartments. This results in the introduction of extra assumptions into the analysis. Another central issue in standard dynamic PET modeling is the difficulty of obtaining a reasonable time course of the concentration of the injected tracer in the blood plasma (i.e., the arterial input function), especially for small animal studies. Thus, the current state-of-the-art in PET kinetic modeling typically requires simplifying assumptions to reduce the number of free parameters and/or nonlinear fitting methods which are well known to be sensitive to
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