Single-voxel delay map from long-axial field-of-view PET scans.

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, namely [O]HO, [F]FDG and [Cu]Cu-DOTATATE, which were used in development and testing of the algorithm. Delay times 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-[O]HO tracers. Testing on synthetically produced time-activity curves showed 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 [O]HO, [F]FDG and [Cu]Cu-DOTATATE, and hence is a viable option offering the ability for delay correction across various organs and tracers in use with kinetic modeling.