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A microvascular compartment model validated using 11C-methylglucose liver PET in pigs

The standard compartment model (CM) is widely used to analyse dynamic PET data. The CM is fitted to time–activity curves to estimate rate constants that describe the transport of a tracer between well-mixed compartments. The aim of this study was to develop and validate a more realistic microvascular compartment model (MCM) that includes capillary tracer concentration gradients, backflux from cells into the perfused capillaries and multiple re-uptakes during the passage through a capillary. The MCM incorporates only parameters with clear physiological meaning, it is easy to implement, and it does not require numerical solution. We compared the MCM and CM for the analysis of 3 min dynamic PET data of pig livers (N  =  5) following injection of 11C-methylglucose. During PET scans, the tracer concentrations in blood were measured in the abdominal aorta, portal vein and liver vein by manual sampling. We found that the MCM outperformed the CM and that dynamic PET data include information which cannot be extracted using standard CM. The MCM fitted dynamic PET data better than the CM (Akaike values were 46  ±  4 for best MCM fits, and 82  ±  8 for best CM fits; mean  ±  standard deviation) and extracted physiologically reasonable parameter estimates such as blood perfusion that were in agreement with independent measurements. The difference between model-independent perfusion estimates and the best MCM perfusion estimates was  −0.01  ±  0.05 ml/ml/min, whereas the difference was 0.30  ±  0.13 ml/ml/min using the CM. In addition, the MCM predicted the time course of concentrations in the liver vein, a prediction fundamentally unobtainable using the CM as it does not return tracer backflux from cells to capillary blood. The results demonstrate the benefit of using models that include more physiology and that models including concentration gradients should be preferred when analysing the blood-cell exchange of any tracer in any capillary bed.