Estimation of input functions from dynamic [18F]FLT PET studies of the head and neck with correction for partial volume effects

Abstract

Background: 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.

Methods: Six 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.

Results: A 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.

Conclusions: Input 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.

Figure 1

Regions of interest in a PET image. (A) A CT slice on which the carotid artery, outlined in red, was delineated. (B) The arterial voxels and external voxels on the corresponding PET image recorded at 2 min. The blood-sampled activity at this time was 12.3 kBq. The arterial voxels containing the carotid artery and surrounding tissue are delineated by the solid black line, and the carotid artery is outlined on the PET voxels in (C). The external tissue, comprising the next layer of voxels out, is delineated by the dashed line.

Figure 2

Seven-parameter function fitted to blood-sampled input function. A blood-sampled input function (solid line), measured for the corresponding PET dataset shown in Figure 1, and the fitted function (dashed line).

Figure 3

Simultaneously estimated vs blood-sampled input functions: two and three blood samples. A blood-sampled input function (solid black line) and input functions simultaneously estimated using two (dashed red line) or three (dashed purple line) blood samples taken at the times indicated by the arrows. The third blood sample was taken at the time indicated by the grey arrow.

Figure 4

Residual error of model as a function of sampling time. RMS _norm value versus sampling times t_k and t_l. The Init_PET values were used as initial values for the estimation process (with simulation of spillover).