Deep learning-based dynamic PET parametric K i image generation from lung static PET.
Haiyan WangYaping WuZhenxing HuangZhicheng LiNa ZhangFangfang FuNan MengHaining WangYun ZhouYongfeng YangXin LiuDong LiangHairong ZhengGreta S P MokMeiyun WangZhanli HuPublished in: European radiology (2022)
imaging has been shown to provide better quantification and improved specificity for cancer detection. • The purpose of this work was to develop a dynamic parametric imaging method based on static PET images using deep learning. • Our proposed network can synthesize highly correlated and consistent dynamic parametric images, providing an additional quantitative diagnostic reference for clinicians.
Keyphrases
- deep learning
- pet imaging
- convolutional neural network
- artificial intelligence
- positron emission tomography
- high resolution
- machine learning
- computed tomography
- pet ct
- papillary thyroid
- palliative care
- squamous cell carcinoma
- mass spectrometry
- loop mediated isothermal amplification
- young adults
- sensitive detection
- lymph node metastasis