Q.Clear Reconstruction for Reducing the Scanning Time for 68Gallium-DOTA-FAPI-04 PET/MR Imaging

crossref(2022)

Cited 0|Views5
No score
Abstract
Abstract Purpose: To determine whether Q.Clear positron emission tomography (PET) reconstruction may reduce tracer injection dose or shorten scanning time in 68Ga fibroblast activation protein inhibitor (FAPI) PET/magnetic resonance (MR) imaging. Methods: We retrospectively collected cases of 68Ga-FAPI whole-body imaging performed on integrated PET/MR. PET images were reconstructed using three different methods: Ordered Subset Expectation Maximization (OSEM) reconstruction with full scanning time, OSEM reconstruction with half scanning time, and Q.Clear reconstruction with half scanning time. We then measured standardized uptake values (SUVs) within and around lesions, alongside their volumes. We also evaluated image quality using lesion-to-background (L/B) ratio and signal to noise ratio (SNR). We then compared these metrics across the three reconstruction techniques using statistical methods. Results: Q.Clear reconstruction significantly increased SUVmax and SUVmean within lesions (by almost 40%) and reduced their volumes in comparison with OSEM reconstruction. Background SUVmax also increased significantly, while background SUVmean showed no difference. Average L/B values for Q.Clear reconstruction were only marginally higher than those from OSME reconstruction with half-time (full-time). SNR decreased significantly in Q.Clear reconstruction compared with OSEM reconstruction with full time (but not half time). Differences between Q.Clear and OSEM reconstructions in SUVmax and SUVmean values within lesions were significantly correlated with SUVs within lesions. Conclusions: Q.Clear reconstruction was useful for reducing PET injection dose or scanning time while maintaining the image quality. Q.Clear may affect PET quantification and it is necessary to establish diagnostic recommendations based on Q.Clear results for Q.Clear application.
More
Translated text
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined