A gate evaluation of the sources of error in quantitative(90) Y PET.

PURPOSE: Accurate reconstruction of the dose delivered by (90) Y microspheres using a postembolization PET scan would permit the establishment of more accurate dose-response relationships for treatment of hepatocellular carcinoma with (90) Y. However, the quality of the PET data obtained is compromised by several factors, including poor count statistics and a very high random fraction. This work uses Monte Carlo simulations to investigate what impact factors other than low count statistics have on the quantification of(90) Y PET.

METHODS: PET acquisitions of two phantoms-a NEMA PET phantom and the NEMA IEC PET body phantom-containing either (90) Y or (18) F were simulated using gate. Simulated projections were created with subsets of the simulation data allowing the contributions of random, scatter, and LSO background to be independently evaluated. The simulated projections were reconstructed using the commercial software for the simulated scanner, and the quantitative accuracy of the reconstruction and the contrast recovery of the reconstructed images were evaluated.

RESULTS: The quantitative accuracy of the (90) Y reconstructions were not strongly influenced by the high random fraction present in the projection data, and the activity concentration was recovered to within 5% of the known value. The contrast recovery measured for simulated (90) Y data was slightly poorer than that for simulated (18) F data with similar count statistics. However, the degradation was not strongly linked to any particular factor. Using a more restricted energy range to reduce the random fraction in the projections had no significant effect.

CONCLUSIONS: Simulations of (90) Y PET confirm that quantitative (90) Y is achievable with the same approach as that used for (18) F, and that there is likely very little margin for improvement by attempting to model aspects unique to (90) Y, such as the much higher random fraction or the presence of bremsstrahlung in the singles data.