Comparison of SUVs based on different ROIs and VOIs definitions: a multicenter respiratory phantom study

Background: In the context of the EORTC LungTech trial, a QA procedure including a PET/CT credentialing has been developed. This procedure will ultimately allow us to pool data from 23 institutions with the overall goal of investigating the impact of tumour motion on quantification. As no standardized procedure exists under respiratory conditions, we investigated the variability of 14 SUV metrics to assess their robustness over respiratory noise. Methods: The customized CIRS-008A phantom was scanned at 13 institutions. This phantom consists of a 18 cm long body, a rod attached to a motion actuator, and a sphere of either 1.5 or 2.5cm diameters. Body, rods and spheres were filled with homogeneous ¹⁸FDG solutions representative of activity concentrations in mediastinum, lung and tumour for a 70kg patient. Three respiratory patterns with peak-to-peak amplitudes and periods of 15mm/3sec, 15mm/6sec and 25mm/4sec were tested. Prior to scanning in respiratory condition, a 3D static PET/CT was acquired as reference. During motion, images were acquired using 3D or 4D gated PET(average image) according to institutional settings. 14 SUV(mean) metrics were obtained per acquisition varying VOI/ ROI shape and location. Three ROIs and three VOIs with respective radii of 0.5, 0.6 and 0.8cm were investigated. These ROIs/VOIs were first centred on the maximum activity voxel; a second analysis was made changing the location from the voxel to the region (ROI5voxels) or the volume (VOI7voxels) with the maximum value. Two additional VOIs were defined as 3D isocontours respectively at 70% and 50% of the maximum voxel value. The SUV metrics were normalized by the corresponding 3D static SUV. Converting to recovery coefficients (RC) allowed us to pool data from all institutions, while maintaining focus solely on motion. For each RC from each motion setting we calculated the mean over institutions, we then looked at the standard deviation (Sd) and spread of each averaged RC over each motion setting (formula [1], [2], Figure1). Results: For the institutions visited we found that RCVOI70% and RCVOI50%, yielded over the 14 metrics the lowest variability to motion with Sd of 0.04 and 0.03 respectively. The RCs based on ROIs/VOIs centered on a single voxel were less impacted by motion (Sd: 0.08) compared to region RCs (Sd: 0.14). The averaged Sd over the RCs based on VOIs and ROIs was 0.12 and 0.11 respectively. Conclusion: Quantification over breathing types depends on ROI/VOI definition. Variables based on SUV max thresholds were found the most robust against respiratory noise.