PET with 18 F-FDG shows promise for the evaluation of metabolic activities in atherosclerotic plaques. Although recommendations regarding the acquisition and measurement protocols to be used for 18 F-FDG PET imaging of atherosclerosis inflammation have been published, there is no consensus regarding the most appropriate protocols, and the image reconstruction approach has been especially overlooked. Given the small size of the targeted lesions, the reconstruction and measurement methods might strongly affect the results. We determined the differences in results due to the protocol variability and identified means of increasing the measurement reliability. Methods: An extensive literature search was performed to characterize the variability in atherosclerosis imaging and quantification protocols. Highly realistic simulations of atherosclerotic carotid lesions based on real patient data were designed to determine how the acquisition and processing protocol parameters affected the measured values. Results: In 49 articles, we identified 53 different acquisition protocols, 51 reconstruction protocols, and 46 quantification methods to characterize atherosclerotic lesions from 18 F-FDG PET images. The most important parameters affecting the measurement accuracy were the number of iterations used for reconstruction and the postfiltering applied to the reconstructed images, which could together make the measured standardized uptake values (SUVs) vary by a factor greater than 3. Image sampling, acquisition duration, and metrics used for the measurements also affected the results to a lesser extent (SUV varying by a factor of 1.3 at most). For an acceptable SUV variability, the lowest bias in SUV was observed using an 8-min acquisition per bed position; ordered-subset expectation maximization reconstruction with at least 120 maximum likelihood expectation maximization equivalent iterations, including a point spread function model using a 1 mm 3 voxel size; and no postfiltering. Because of the partial-volume effect, measurement bias remained greater than 60%. The use and limitations of the target-to-blood activity ratio metrics are also presented and discussed. Conclusion: 18 F-FDG PET protocol harmonization is needed in atherosclerosis imaging. Optimized protocols can significantly reduce the measurement errors in wall activity estimates, but PET systems with higher spatial resolution and advanced partial-volume corrections will be required to accurately assess plaque inflammation from 18 F-FDG PET.

We evaluated the impact of partial volume effect (PVE) in the assessment of arterial diseases with (18)FDG PET. An anthropomorphic digital phantom enabling the modeling of aorta related diseases like atherosclerosis and arteritis was used. Based on this phantom, we performed GATE Monte Carlo simulations to produce realistic PET images with a known organ segmentation and ground truth activity values. Images corresponding to 15 different activity-concentration ratios between the aortic wall and the blood and to 7 different wall thicknesses were generated. Using the PET images, we compared the theoretical wall-to-blood activity-concentration ratios (WBRs) with the measured WBRs obtained with five measurement methods: (1) measurement made by a physician (Expert), (2) automated measurement supposed to mimic the physician measurements (Max), (3) simple correction based on a recovery coefficient (Max-RC), (4) measurement based on an ideal VOI segmentation (Mean-VOI) and (5) measurement corrected for PVE using an ideal geometric transfer matrix (GTM) method. We found that Mean-VOI WBRs values were strongly affected by PVE. WBRs obtained by the physician measurement, by the Max method and by the Max-RC method were more accurate than WBRs obtained with the Mean-VOI approach. However Expert, Max and Max-RC WBRs strongly depended on the wall thickness. Only the GTM corrected WBRs did not depend on the wall thickness. Using the GTM method, we obtained more reproducible ratio values that could be compared across wall thickness. Yet, the feasibility of the implementation of a GTM-like method on real data remains to be studied.

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