Abstract
BackgroundWe present a method for extracting arterial input functions from dynamic [18F]FLT PET images of the head and neck, directly accounting for the partial volume effect. The method uses two blood samples, for which the optimum collection times are assessed.MethodsSix datasets comprising dynamic PET images, co-registered computed tomography (CT) scans and blood-sampled input functions were collected from four patients with head and neck tumours. In each PET image set, a region was identified that comprised the carotid artery (outlined on CT images) and surrounding tissue within the voxels containing the artery. The time course of activity in the region was modelled as the sum of the blood-sampled input function and a compartmental model of tracer uptake in the surrounding tissue.The time course of arterial activity was described by a mathematical function with seven parameters. The parameters of the function and the compartmental model were simultaneously estimated, aiming to achieve the best match between the modelled and imaged time course of regional activity and the best match of the estimated blood activity to between 0 and 3 samples. The normalised root-mean-square (RMSnorm) differences and errors in areas under the curves (AUCs) between the measured and estimated input functions were assessed.ResultsA one-compartment model of tracer movement to and from the artery best described uptake in the tissue surrounding the artery, so the final model of the input function and tissue kinetics has nine parameters to be estimated. The estimated and blood-sampled input functions agreed well when two blood samples, obtained at times between 2 and 8 min and between 8 and 60 min, were used in the estimation process (RMSnorm values of 1.1 ± 0.5 and AUC errors for the peak and tail region of the curves of 15% ± 9% and 10% ± 8%, respectively). A third blood sample did not significantly improve the accuracy of the estimated input functions.ConclusionsInput functions for FLT-PET studies of the head and neck can be estimated well using a one-compartment model of tracer movement and TWO blood samples obtained after the peak in arterial activity.
Highlights
We present a method for extracting arterial input functions from dynamic [18F]3 -deoxy-3 -[18F]fluoro-thymidine (FLT) positron emission tomography (PET) images of the head and neck, directly accounting for the partial volume effect
Other sources of arterial input functions have been explored, such as Time-activity curve (TAC) taken from the vicinity of large blood vessels in dynamic PET images [6,7]
Simultaneous estimation of the arterial input function and surrounding tissue TAC Table 2 summarises the metrics of agreement between the blood-sampled and estimated input functions when blood samples were taken at times found to minimise RMSnorm values
Summary
We present a method for extracting arterial input functions from dynamic [18F]FLT PET images of the head and neck, directly accounting for the partial volume effect. The method uses two blood samples, for which the optimum collection times are assessed. The gold standard method used to determine blood TACs is continuous arterial sampling, which is invasive, requires expensive equipment and carries a small risk to patients [5]. Other sources of arterial input functions have been explored, such as TACs taken from the vicinity of large blood vessels in dynamic PET images [6,7]. Accurate determination of input functions from PET images is hindered by the ‘partial volume effect’ (PVE), a term that refers to two distinct but related phenomena: spillover and the tissue-fraction effect [8]. The tissue-fraction effect arises because the activity reported in each PET voxel is an average of the corresponding volume, which usually comprises tissues of different types and activity concentrations
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