Abstract
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 (PET20m) and short-time scanning PET (PET2m) images. We generated a standard-time scanning PET-like image (sPET20m) from a PET2m image using a deep-learning network. We did qualitative and quantitative analyses to assess whether the sPET20m images were available for clinical applications. In our internal validation, sPET20m images showed substantial improvement on all quality metrics compared with the PET2m images. There was a small mean difference between the standardized uptake value ratios of sPET20m and PET20m 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.
Highlights
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
Deep-learning techniques for image restoration have been widely applied to medical images, including computed tomography (CT), magnetic resonance imaging (MRI), and PET3–11
We investigated the feasibility of a deep-learning-based reconstruction approach using short-time acquisition PET scans
Summary
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 can generate interpretable PET images from low-quality PET images because of the short scanning time using deep-learning techniques. Amyloid positron emission tomography (PET) is a nuclear medicine imaging test that shows amyloid deposits in the brain It is currently being used in the diagnosis of Alzheimer’s disease, which is known to be caused by amyloid[1]. There have been only a few studies on reducing noise and improving the quality of images taken by reducing the acquisition time of brain PET7 They have used additional MR information obtained from a PET/ MR scanner to restore brain PET images. Since PET/CT scanners are used in most hospitals, a restoration technique using only PET without MRI information is needed
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