A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET

Purpose A critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from lowdose scans on different scanners and tracers. Methods Brain [F-18]FDG PET imaging of 237 patients scanned with one scanner was used for the development of AI technology. The developed algorithm was then tested on [F-18]FDG PET images of 45 patients scanned with three different scanners, [F-18]FET PET images of 18 patients scanned with two different scanners, as well as [F-18]Florbetapir images of 10 patients. A conditional generative adversarial network (GAN) was customized for cross-scanner and cross-tracer optimization. Three nuclear medicine physicians independently assessed the utility of the results in a clinical setting. Results The improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = -0.71, p < 0.05) and normalized dose acquisition (r = -0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05). Conclusion The deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.

Automated summary

The study aimed to develop an AI that can independently recover high-quality imaging from low-dose scans on different scanners and tracers. The developed AI showed significant improvement in image quality and utility based on physical and clinical assessments, which could enhance the trustworthiness of AI-based dose reduction in clinical practice.