Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging

Purpose Tendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and increase patient comfort. This work aims to assess the performance of regular full-dose (FD) synthesis from fast/low-dose (LD) whole-body (WB) PET images using deep learning techniques. Methods Instead of using synthetic LD scans, two separate clinical WB F-18-Fluorodeoxyglucose (F-18-FDG) PET/CT studies of 100 patients were acquired: one regular FD (similar to 27 min) and one fast or LD (similar to 3 min) consisting of 1/8(th) of the standard acquisition time. A modified cycle-consistent generative adversarial network (CycleGAN) and residual neural network (ResNET) models, denoted as CGAN and RNET, respectively, were implemented to predict FD PET images. The quality of the predicted PET images was assessed by two nuclear medicine physicians. Moreover, the diagnostic quality of the predicted PET images was evaluated using a pass/fail scheme for lesion detectability task. Quantitative analysis using established metrics including standardized uptake value (SUV) bias was performed for the liver, left/right lung, brain, and 400 malignant lesions from the test and evaluation datasets. Results CGAN scored 4.92 and 3.88 (out of 5) (adequate to good) for brain and neck + trunk, respectively. The average SUV bias calculated over normal tissues was 3.39 +/- 0.71% and - 3.83 +/- 1.25% for CGAN and RNET, respectively. Bland-Altman analysis reported the lowest SUV bias (0.01%) and 95% confidence interval of - 0.36, + 0.47 for CGAN compared with the reference FD images for malignant lesions. Conclusion CycleGAN is able to synthesize clinical FD WB PET images from LD images with 1/8th of standard injected activity or acquisition time. The predicted FD images present almost similar performance in terms of lesion detectability, qualitative scores, and quantification bias and variance.

Automated summary

The study aimed to evaluate the performance of synthesizing regular full-dose PET images from fast/low-dose whole-body PET images using deep learning techniques. The results showed that the predicted full-dose images had almost similar performance in terms of lesion detectability, qualitative scores, and quantification bias and variance.