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

• Published in: EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING Vol. 50; no. 2; pp. 257 - 265
• Main Authors: Sari, Hasan, Eriksson, Lars, Mingels, Clemens, Alberts, Ian, Casey, Michael E., Afshar-Oromieh, Ali, Conti, Maurizio, Cumming, Paul, Shi, Kuangyu, Rominger, Axel
• Format: Journal Article Publication
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2023; Springer Nature B.V
• Subjects: Aorta; Arteries; Bed movements; Bias; Blood; Body measurements; Cardiology; Coronary vessels; Data acquisition; Datasets; Estimates; Feasibility; Feasibility Studies; Fluorine isotopes; Fluorodeoxyglucose F18; Humans; Image quality; Image reconstruction; Imaging; Lesions; Medicine; Medicine & Public Health; Modelling; Neoplasms - diagnostic imaging; Noise levels; Nuclear Medicine; Oncology; Original; Original Article; Orthopedics; Performance evaluation; Positron emission; Positron emission tomography; Positron-Emission Tomography - methods; Radiology; Scanners; Signal to noise ratio; Statistical analysis; Substantia grisea; Technology; Temporal resolution; Thorax; Tumors; FDG; LAFOV PET; Kinetic modeling; Parametric imaging
• ISSN: 1619-7070; 1619-7089; 1619-7089
• DOI: 10.1007/s00259-022-05983-7

Background Accurate kinetic modeling of 18F-fluorodeoxyglucose ([ 18 F]-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 the use of non-invasive image-derived input functions (IDIFs) 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 (PBIFs) have previously shown potential in accurate Patlak analysis of [ 18 F]-FDG datasets and can enable the use of shortened dynamic imaging protocols. Here, we exploit the high sensitivity and temporal resolution of a LAFOV PET system and explore the use of PBIF with abbreviated protocols in [ 18 F]-FDG total body kinetic modeling. Methods Dynamic PET data were acquired in 24 oncological subjects for 65 min following the administration of [ 18 F]-FDG. IDIFs were extracted from the descending thoracic aorta, and a PBIF was generated from 16 datasets. Five different scaled PBIFs (sPBIFs) were generated by scaling the PBIF with the 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 K i estimates in tumor lesions and cerebral gray matter. Patlak plot start time ( t *) was also varied to evaluate the performance of shorter acquisitions on the accuracy of Patlak K i estimates. Patlak K i estimates with IDIF and t * = 35 min were used as reference, and mean bias and precision (standard deviation of bias) were calculated to assess the relative performance of different sPBIFs. A 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). Excellent agreement was shown between Patlak K i estimates obtained using sPBIF and IDIF. Using the sPBIF 55–65 with the Patlak model, 20 min of PET data (i.e., 45 to 65 min post-injection) achieved < 15% precision error in K i estimates in tumor 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 K i generated with an IDIF and 30 min of PET data as reference, Patlak K i images generated using sPBIF 55–65 with 20 min 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 [ 18 F]-FDG Patlak analysis using sPBIFs with only 20 min of PET data from a LAFOV PET scanner.