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.