Restoration of amyloid PET images obtained with short-time data using a generative adversarial networks framework

Our purpose in this study is to evaluate the clinical feasibility of deep-learning techniques for F-18 florbetaben (FBB) positron emission tomography (PET) image reconstruction using data acquired in a short time. We reconstructed raw FBB PET data of 294 patients acquired for 20 and 2 min into standard-time scanning PET (PET(20m)) and short-time scanning PET (PET(2m)) images. We generated a standard-time scanning PET-like image (sPET(20m)) from a PET(2m) image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET(20m) images were available for clinical applications. In our internal validation, sPET(20m) images showed substantial improvement on all quality metrics compared with the PET(2m) images. There was a small mean difference between the standardized uptake value ratios of sPET(20m) and PET(20m) images. A Turing test showed that the physician could not distinguish well between generated PET images and real PET images. Three nuclear medicine physicians could interpret the generated PET image and showed high accuracy and agreement. We obtained similar quantitative results by means of temporal and external validations. We can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Although more clinical validation is needed, we confirmed the possibility that short-scanning protocols with a deep-learning technique can be used for clinical applications.