Development and evaluation of a bayesian optimization FDG population-based input function for implementing parametric...

Aim.Parametric imaging from dynamic positron emission tomography (PET) has gained interest for tumour diagnostics and treatment response evaluation. However, the lack of a standardized method for generating theinput function-reference curve for kinetic modelling-has led to inconsistent descriptors, contributing to uncertainties in parametric imaging reliability. This study aims to address this challenge by proposing a hyperparametric optimization method for deriving FDG population-based input function (PBIF), independent of acquisition and injection protocols.Method. This study included ten patients undergoing FDG PET scans using a standard axial field of view scanner. Image-derived input functions (IDIF) were extracted from the descending aorta, normalized, and utilized as input for PBIF modelling. Bayesian hyperparameter optimization was employed to estimate global optima for ten parameters that describe the input function through independent runs of up to 600 iterations each. The injection profile was integrated as a double rectangular profile, representing both the tracer injection and the saline flush tracer residual.Results. The Bayesian optimization successfully modelled patient-specific IDIFs (R² = 0.97). The algorithm estimated injection and flush durations in agreement with recorded values. Parameter distributions showed low variability, with median amplitude and time constant values varying by around 15%. The glucose-affine molecule dynamics were characterized by distinct time constants of 6 s, 4 min, and 70 min. Analytical and numerical comparisons of parametric imaging from IDIF and PBIF show that Patlak analysis is unaffected by the injection characteristics.Conclusion. The study highlights the benefits of Bayesian optimization for modelling PBIF without prior knowledge of injection characteristics. These findings support the existence of unified FDG PBIF, facilitating the utilization of parametric imaging across PET centres. Although the present study is based on a limited, single-centre cohort, this methodological development is intended as a foundational study to further multi-centre validation on larger population.