Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods

作者：Yiyi Hu, Doudou Lv, Shaojie Jian, Limin Lang, Caozhe Cui, Meng Liang, Liwei Song, Sijin Li, Zhifang Wu · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2023 · DOI：10.21037/qims-22-1181 · 被引用次数：8 · 研究领域：Medical Imaging Techniques and Applications、Radiomics and Machine Learning in Medical Imaging、Advanced Radiotherapy Techniques

Background: F] Fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) is an important tool for tumor assessment. Shortening scanning time and reducing the amount of radioactive tracer remain the most difficult challenges. Deep learning methods have provided powerful solutions, thus making it important to choose an appropriate neural network architecture. Methods: F-FDG PET/CT were retrospectively collected. The PET collection time was 3 min/bed. The first 15 and 30 s of each bed collection time were selected to simulate low-dose collection, and the pre-90s was used as the clinical standard protocol. Low-dose PET was used as input, convolutional neural network (CNN, 3D Unet as representative) and generative adversarial network (GAN, P2P as representative) were used to predict the full-dose images. The image visual scores, noise levels and quantitative parameters of tumor tissue were compared. Results: =1,325.46, P<0.001). Both deep learning models reduced the standard deviation (SD) of background, and increased the signal-to-noise ratio (SNR). When 8%PET images were used as input, P2P and 3D Unet had similar enhancement effect on SNR of tumor lesions, but 3D Unet could significantly improve the contrast-noise ratio (CNR) (P<0.05). There was no significant difference in SUVmean of tumor lesions compared with s-PET group (P>0.05). When 17%PET image was used as input, SNR, CNR and SUVmax of tumor lesion of 3D Unet group had no statistical difference wi...