Combining deep learning with a kinetic model to predict dynamic PET images and generate parametric images

EJNMMI Physics

Original Research

Ying Liang¹  Xieraili Wumener¹  Jinpeng Zhou¹  Yarong Zhang¹  Zixiang Chen²  Dong Liang²  Liwen Wan²  Zhanli Hu²  Ganglin Liang³ 

[1] Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 518116, Shenzhen, China;Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China;Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China;Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China;

关键词: Parametric imaging;    Image generation;    Deep learning;    Kinetic model;    Dynamic PET images;   

DOI  :  10.1186/s40658-023-00579-y

 received in 2023-01-19, accepted in 2023-09-15,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundDynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (Ki) of tissues using graphical methods (such as Patlak plots). Ki is more stable than the standard uptake value and has a good reference value for clinical diagnosis. However, the long scanning time required for obtaining dynamic PET images, usually an hour, makes this method less useful in some ways. There is a tradeoff between the scan durations and the signal-to-noise ratios (SNRs) of Ki images. The purpose of our study is to obtain approximately the same image as that produced by scanning for one hour in just half an hour, improving the SNRs of images obtained by scanning for 30 min and reducing the necessary 1-h scanning time for acquiring dynamic PET images.MethodsIn this paper, we use U-Net as a feature extractor to obtain feature vectors with a priori knowledge about the image structure of interest and then utilize a parameter generator to obtain five parameters for a two-tissue, three-compartment model and generate a time activity curve (TAC), which will become close to the original 1-h TAC through training. The above-generated dynamic PET image finally obtains the Ki parameter image.ResultsA quantitative analysis showed that the network-generated Ki parameter maps improved the structural similarity index measure and peak SNR by averages of 2.27% and 7.04%, respectively, and decreased the root mean square error (RMSE) by 16.3% compared to those generated with a scan time of 30 min.ConclusionsThe proposed method is feasible, and satisfactory PET quantification accuracy can be achieved using the proposed deep learning method. Further clinical validation is needed before implementing this approach in routine clinical applications.

【 授权许可】

CC BY   
© Springer Nature Switzerland AG 2023

【 图 表 】

12951_2016_246_Article_IEq13.gif

12951_2016_246_Article_IEq12.gif

Fig. 1

Fig. 1

Fig. 9

Fig. 4

12951_2017_255_Article_IEq52.gif

12951_2017_255_Article_IEq51.gif

Fig. 4

Fig. 2

Fig. 1

Fig. 1

Fig. 4

Fig. 1

Fig. 2

12951_2016_223_Article_IEq1.gif

Fig. 5

12951_2016_246_Article_IEq9.gif

12951_2017_303_Article_IEq1.gif

Fig. 2

12951_2015_155_Article_IEq70.gif

Fig. 10

Fig. 7

Fig. 5

Fig. 1

Fig. 19

12951_2017_255_Article_IEq45.gif

12888_2023_5206_Article_IEq3.gif

Fig. 2

Fig. 13

Fig. 1

Fig. 12

Fig. 8

Fig. 2

Fig. 2

Fig. 2

Fig. 1

Fig. 5

Fig. 4

Fig. 2