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

Hackett SL, Liu D, Chalkidou A, Marsden P, Landau D, Fenwick JD - EJNMMI Res (2013)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gray Institute for Radiation Oncology and Biology, Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK. sara.hackett@oncology.ox.ac.uk.

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.

No MeSH data available.


Related in: MedlinePlus

Residual error of model as a function of sampling time. RMSnorm value versus sampling timestk and tl. TheInitPET values were used as initial valuesfor the estimation process (with simulation of spillover).
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Figure 4: Residual error of model as a function of sampling time. RMSnorm value versus sampling timestk and tl. TheInitPET values were used as initial valuesfor the estimation process (with simulation of spillover).

Mentions: In contrast, the accuracy of input functions estimated using two or three arterialconcentration measurements was independent of initial parameter values. The lowestRMS norm values were obtained using combinations of intermediate andlate times of concentration measurement, as shown in Figure 4. Inclusion of the second concentration measurement taken in theintermediate interval improved the agreement between the blood-sampled andestimated input functions in the peak region without reducing the agreement in thetail region.


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

Hackett SL, Liu D, Chalkidou A, Marsden P, Landau D, Fenwick JD - EJNMMI Res (2013)

Residual error of model as a function of sampling time. RMSnorm value versus sampling timestk and tl. TheInitPET values were used as initial valuesfor the estimation process (with simulation of spillover).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109699&req=5

Figure 4: Residual error of model as a function of sampling time. RMSnorm value versus sampling timestk and tl. TheInitPET values were used as initial valuesfor the estimation process (with simulation of spillover).
Mentions: In contrast, the accuracy of input functions estimated using two or three arterialconcentration measurements was independent of initial parameter values. The lowestRMS norm values were obtained using combinations of intermediate andlate times of concentration measurement, as shown in Figure 4. Inclusion of the second concentration measurement taken in theintermediate interval improved the agreement between the blood-sampled andestimated input functions in the peak region without reducing the agreement in thetail region.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Gray Institute for Radiation Oncology and Biology, Department of Oncology, University of Oxford, Oxford OX3 7DQ, UK. sara.hackett@oncology.ox.ac.uk.

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.

No MeSH data available.


Related in: MedlinePlus