Ultralow-dose Pediatric Total-body PET/CT Imaging Using an Artificial Intelligence Technique

Abstract Purpose Young children are more sensitive to radiation than adults, and their absorption of effective dose can be four times higher than that of adults, inducing a higher risk of secondary injury. Here, we propose for the first time the use of artificial intelligence techniques combined with low dose CT prior information to improve image quality in ultralow-dose total-body PET/CT scans.MethodsA total of 44 pediatric patients (weight range: 8·5–50·1 kg; ages 1–12 years) who underwent total-body PET/CT at the Sun Yat-sen University Cancer Center were retrospectively enrolled. 18F-FDG was administered at a dose of 3·7 MBq/kg and an acquisition of 600 s. The low-dose PET images were simulated by truncating the list-mode data to reduce the count density. The neural network uses the residual network as the basic structure and fuses low-dose CT images as the priori information into the network at different scales. The image quality was assessed by subjective and objective analyses. Bland-Altman plots were used to assess the agreement of regional SUV ratios between the image types. Statistical analysis was carried out to assess the differences in the image quality metrics and reader agreement.ResultsThe use of artificial intelligence techniques can significantly improve PET image quality. When combined with a prior CT information, the anatomical information of the images was better recovered, and the 15 seconds acquisition yields a quality equivalent to the 10 minutes acquisition, it can equivalently guide the concentration of the injected tracer to decrease, which is very important for dose-sensitive pediatric patients.ConclusionsThe proposed artificial intelligence technology is safe and can effectively enhance the quality of pediatric total-body PET/CT ultralow-dose images and has the potential to further reduce the concentration of injected tracers for clinical applications.