Image-derived input function for assessment of 18F-FDG uptake by the inflamed lung

Pulmonary uptake of (18)F-FDG assessed with PET has been used to quantify the metabolic activity of inflammatory cells in the lung. This assessment involves modeling of tracer kinetics and knowledge of a time-activity curve in pulmonary artery plasma as an input function, usually acquired by manual blood sampling. This paper presents and validates a method to accurately derive an input function from a blood-pool region of interest (ROI) defined in dynamic PET images.

The method is based on a 2-parameter model describing the activity of blood and that from spillover into the time-activity curve for the ROI. The model parameters are determined using an iterative algorithm, with 2 blood samples used to calibrate the raw PET-derived activity data. We validated both the 2-parameter model and the method to derive a quantitative input function from ROIs defined for the cavities of the right and left heart and for the descending aorta by comparing them against the time-activity curve obtained by manual blood sampling from the pulmonary artery in lungs with acute inflammation.

The model accurately described the time-activity curve from sampled blood. The 2-sample calibration method provided an efficient algorithm to derive input functions that were virtually identical to those sampled manually, including the fast kinetics of the early phase. The (18)F-FDG uptake rates in acutely injured lungs obtained using this method correlated well with those obtained exclusively using manual blood sampling (R(2) > 0.993). Within some bounds, the model was found quite insensitive to the timing of calibration blood samples or the exact definition of the blood-pool ROIs.

Using 2 mixed venous blood samples, the method accurately assesses the entire time course of the pulmonary (18)F-FDG input function and does not require the precise geometry of a specific blood-pool ROI or a population-based input function. This method may substantially facilitate studies involving modeling of pulmonary (18)F-FDG in patients with viral or bacterial infections, pulmonary fibrosis, and chronic obstructive pulmonary disease.