Purpose: Most data-driven gating (DDG) PET applications utilize ≤50% of all PET counts, increasing intrinsic image noise. Misregistration between DDG-PET and CT can also impede the full benefits of motion correction for PET quantification. In this work, the separate influences of noise, motion correction, and accurate attenuation correction (AC) on PET quantification were assessed.
Methods: Twelve min of PET data and a low-dose cine CT were acquired in regions with primary lesions and expected respiratory motion. DDG-PET/CT was achieved with GE’s Q.Static at 50% counts in the end-expiration phase and end-expiration CT derived from cine CT using an in-house algorithm. Simulated reconstructions from 30 sec to 12 min were created, with both the helical-CT and DDG-CT used for AC. SUVmax and SUVpeak were analyzed for 45 liver or lung lesions from 27 cases. SUV bias and variability from changes in total counts were assessed with repeated-measures one-way ANOVA and Bland-Altman analysis (limits of agreement; LoA = mean±95% prediction interval; 1.96σ, PI).
Results: 50% reduced counts produced intrinsic variabilities in SUVmax and SUVpeak with no clear bias (mean Δ: +0.5%, ‒0.7%) but distinct magnitudes (95% PI: ±13%, ±5%). When counts were reduced further to 12.5%, a positive SUVmax bias was established (bias: +6±16%; p=0.03). SUVpeak showed no clear bias at any reduction in counts, even down to <5%. On average, improved AC with DDG-CT increased SUVmax and SUVpeak 15% beyond that from DDG-PET alone. With 50% counts DDG-PET/CT, there were 27 (60%) and 35 (78%) lesions with SUVmax and SUVpeak increases above the maximum LoA established from intrinsic noise effects.
Conclusion: The common application of 50% counts DDG-PET did not lead to inaccurate or biased SUV – the observed increases resulted from gating. DDG-CT was at least equally as important as motion correction with DDG-PET for increasing SUV in DDG-PET/CT.
Funding Support, Disclosures, and Conflict of Interest: This research was supported in-part by NIH grants R21-CA222749-01A1, R03-EB030280-01, R01HL157273-01, and a ROSI grant from the UT M.D. Anderson Cancer Center. This research was conducted at the Center for Advanced Biomedical Imaging in-part with equipment support from GE Healthcare. T. Pan is a consultant for Bracco Medical Systems, LLC.