Feasibility of using abbreviated scans protocols with population-based input functions for accurate kinetic modelling of 18F-FDG datasets from a long-axial FOV PET scanner

Abstract

Background: Accurate kinetic modelling of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) data requires accurate knowledge of the available tracer concentration in the plasma during the scan time, known as the arterial input function (AIF). The gold standard method to derive the AIF requires collection of serial arterial blood samples but the introduction of long axial field of view (LAFOV) PET systems enables use of non-invasive image derived input functions (IDIF) from large blood pools such as the aorta without any need for bed movement. However, such protocols require a prolonged dynamic PET acquisition which is impractical in a busy clinical setting. Population-based input functions (PBIF) have previously shown potential in accurate Patlak analysis of 18F-FDG datasets and can enable the use of shortened dynamic imaging protocols. We not exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore use of PBIF with abbreviated protocols in 18F-FDG total body kinetic modelling. Methods: Dynamic PET data were acquired in 24 oncological subjects for 65 minutes following the administration of 18F-FDG. IDIFs were extracted from the descending thoracic aorta and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIF) were generated by scaling the PBIF with AUC of IDIF curve tails using various portions of image data (35-65, 40-65, 45-65, 50-65 and 55-65 min post injection). The sPBIFs were compared with the IDIFs using the AUCs and Patlak Ki estimates in tumour lesions and cerebral grey matter. Patlak plot start time (t*) was also varied to evaluate the performance of shorter acquisitions on accuracy of Patlak Ki estimates. Patlak Ki estimates with IDIF and t*=35 min was used as reference and mean bias and precision (standard deviation of bias) were calculated to assess relative performance of different sPBIFs. Comparison of parametric images generated using IDIF and sPBIFs was also performed. Results: There was no statistically significant difference between AUCs of the IDIF and sPBIFs (Wilcoxon test: P>0.05). The sPBIF55-65 showed the best performance with 1.5% bias and %6.8 precision in tumour lesions. Using the sPBIF55-65 with Patlak model, 20 minutes of PET data (i.e. 45 to 65 min post injection) achieved <15% precision error in Ki estimates in tumour lesions compared to the estimates with the IDIF. Parametric images reconstructed using the IDIF and sPBIFs with and without an abbreviated protocol were visually comparable. Using Patlak Ki generated with an IDIF and 30 mins of PET data as reference, Patlak Ki images generated using sPBIF55-65 with 20 minutes of PET data (t*=45 min) provided excellent image quality with structural similarity index measure > 0.99 and peak signal-to-noise ratio > 55 dB. Conclusion: We demonstrate the feasibility of performing accurate 18F-FDG Patlak analysis using sPBIFs with only 20 minutes of PET data from a LAFOV PET scanner.

Reduced Scanning-Time with a Dual-Injection Protocol for Dynamic Whole-Body PET

“…A relative Patlak approach was presented, which simply ignores the initial part of the AIF, and can be useful if absolute values are not required [4]. Several scanning protocols with a late-start time have also been proposed in combination with a population-based input function [5]- [7]. These allow for absolute quantification but may suffer from limited accuracy.…”