Source‐to‐Target Automatic Rotating Estimation (STARE) – A publicly‐available, blood‐free quantification approach for PET tracers with irreversible kinetics: Theoretical framework and validation for [18F]FDG

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2022-04, Vol.249, p.118901-118901, Article 118901
• Main Authors: Bartlett, Elizabeth A, Ogden, R Todd, Mann, J John, Zanderigo, Francesca
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; Blood; Blood-free PET quantification; Brain research; Cerebellum - diagnostic imaging; Cerebral Cortex - diagnostic imaging; Electronic health records; Fluorodeoxyglucose F18 - pharmacokinetics; Glucose; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Irreversible radiotracers; Kinetic modeling; Kinetics; Machine learning; Metabolism; Models, Theoretical; Net influx rate; Positron emission tomography; Positron-Emission Tomography - methods; Positron-Emission Tomography - standards; Radiopharmaceuticals - pharmacokinetics; Source-to-target modeling
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2022.118901

Full quantification of positron emission tomography (PET) data requires an input function. This generally means arterial blood sampling, which is invasive, labor-intensive and burdensome. There is no current, standardized method to fully quantify PET radiotracers with irreversible kinetics in the absence of blood data. Here, we present Source-to-Target Automatic Rotating Estimation (STARE), a novel, data-driven approach to quantify the net influx rate (Ki) of irreversible PET radiotracers, that requires only individual-level PET data and no blood data. We validate STARE with human [18F]FDG PET scans and assess its performance using simulations. STARE builds upon a source-to-target tissue model, where the tracer time activity curves (TACs) in multiple “target” regions are expressed at once as a function of a “source” region, based on the two-tissue irreversible compartment model, and separates target region Ki from source Ki by fitting the source-to-target model across all target regions simultaneously. To ensure identifiability, data-driven, subject-specific anchoring is used in the STARE minimization, which takes advantage of the PET signal in a vasculature cluster in the field of view (FOV) that is automatically extracted and partial volume-corrected. To avoid the need for any a priori determination of a single source region, each of the considered regions acts in turn as the source, and a final Ki is estimated in each region by averaging the estimates obtained in each source rotation. In a large dataset of human [18F]FDG scans (N = 69), STARE Ki estimates were correlated with corresponding arterial blood-based Ki estimates (r = 0.80), with an overall regression slope of 0.88, and were precisely estimated, as assessed by comparing STARE Ki estimates across several runs of the algorithm (coefficient of variation across runs=6.74 ± 2.48%). In simulations, STARE Ki estimates were largely robust to factors that influence the individualized anchoring used within its algorithm. Through simulations and application to [18F]FDG PET data, feasibility is demonstrated for STARE blood-free, data-driven quantification of Ki. Future work will include applying STARE to PET data obtained with a portable PET camera and to other irreversible radiotracers.