Single-time-point dosimetry using model selection and the Bayesian fitting method: A proof of concept.

Purpose: This study aimed to determine the effect of model selection on simplified dosimetry for the kidneys using Bayesian fitting (BF) and single-time-point (STP) imaging.

Methods: Kidney biokinetics data of [¹⁷⁷Lu]Lu-PSMA-617 from mHSPC were collected using SPECT/CT after injection of (3.1 ± 0.1) GBq at time points T1(2.3 ± 0.5), T2(23.8 ± 2.0), T3(47.7 ± 2.2), T4(71.8 ± 2.2), and T5(167.4 ± 1.9) h post-injection. Eleven functions with various parameterizations and a combination of shared and individual parameters were used for model selection. Model averaging of functions with an Akaike weight of >10 % was used to calculate the reference TIAC (TIAC_REF). STP BF method (STP-BF) was performed to determine the STP TIACs (TIAC_STP-BF). The STP-BF performance was assessed by calculating the root-mean-square error (RMSE) of relative deviation between TIAC_STP-BF and TIAC_REF. In addition, the STP-BF performance was compared to the Hänscheid Method.

Results: The function [Formula: see text] with shared parameter λ₂ was selected as the best function (Akaike weight of 57.91 %). STP-BF using the best function resulted in RMSEs of 20.3 %, 9.1 %, 8.4 %, 13.6 %, and 19.3 % at T1, T2, T3, T4, and T5, respectively. The RMSEs of STP-Hänscheid were 22.4 %, 14.6 %, and 21.9 % at T2, T3, and T4, respectively.

Conclusion: A model selection was presented to determine the fit function for calculating TIACs in STP-BF. This study shows that the STP dosimetry using BF and model selection performed better than the frequently used STP Hänscheid method.