Objective: We present an algorithm to estimate the delay between a tissue time activity curve and a blood input curve at a single-voxel level tested on whole-body data from a long-axial field-of-view scanner with tracers of different noise characteristics.

Methods: Whole-body scans of 15 patients divided equally among three tracers: [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, were used in development and testing of the algorithm. Delay time were estimated by fitting the cumulatively summed input function and tissue time activity curve with special considerations for noise. To evaluate the performance of the algorithm, it was compared against two other algorithms also commonly applied in delay estimation, name cross-correlation and a one-tissue compartment model with incorporated delay. All algorithms were tested on both synthetic time activity curves produced with the one-tissue compartment model with increasing levels of noise and delays between the tissue activity curve and the blood input curve. Whole-body delay maps were also calculated for each of the three tracers with data acquired on a long-axial field-of-view scanner with high time resolution.

Results: Our proposed model performs better for low signal-to-noise ratio time activity curves compared to both cross-correlation and the one-tissue compartment models for non-[15O]H2O tracers. Testing on synthetically produced time activity curves it displays only a small and even residual delay, while the one-tissue compartment model with included delay showed varying residual delays.

Conclusion: The algorithm is robust to noise and proves applicable on a range of tracers as tested on [15O]H2O, [18F]FDG and [64Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.