F-FDG dose reduction using deep learning-based PET reconstruction.

BACKGROUND

A deep learning-based image reconstruction (DLR) algorithm that can reduce the statistical noise has been developed for PET/CT imaging. It may reduce the administered dose of F-FDG and minimize radiation exposure while maintaining diagnostic quality. This retrospective study evaluated whether the injected F-FDG dose could be reduced by applying DLR to PET images. To this aim, we compared the quantitative image quality metrics and the false-positive rate between DLR with a reduced F-FDG dose and Ordered Subsets Expectation Maximization (OSEM) with a standard dose.

RESULTS

This study included 90 oncology patients who underwent F-FDG PET/CT. They were divided into 3 groups (30 patients each): group A (F-FDG dose per body weight [BW]: 2.00-2.99 MBq/kg; PET image reconstruction: DLR), group B (3.00-3.99 MBq/kg; DLR), and group C (standard dose group; 4.00-4.99 MBq/kg; OSEM). The evaluation was performed using the signal-to-noise ratio (SNR), target-to-background ratio (TBR), and false-positive rate. DLR yielded significantly higher SNRs in groups A and B than group C (p < 0.001). There was no significant difference in the TBR between groups A and C, and between groups B and C (p = 0.983 and 0.605, respectively). In group B, more than 80% of patients weighing less than 75 kg had at most one false positive result. In contrast, in group B patients weighing 75 kg or more, as well as in group A, less than 80% of patients had at most one false-positives.

CONCLUSIONS

Our findings suggest that the injected F-FDG dose can be reduced to 3.0 MBq/kg in patients weighing less than 75 kg by applying DLR. Compared to the recommended dose in the European Association of Nuclear Medicine (EANM) guidelines for 90 s per bed position (4.7 MBq/kg), this represents a dose reduction of 36%. Further optimization of DLR algorithms is required to maintain comparable diagnostic accuracy in patients weighing 75 kg or more.