Liang Ganglin, Zhou Jinpeng, Chen Zixiang, Wan Liwen, Wumener Xieraili, Zhang Yarong, Liang Dong, Liang Ying, Hu Zhanli
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Beijing, China.
EJNMMI Phys. 2023 Oct 24;10(1):67. doi: 10.1186/s40658-023-00579-y.
Dynamic positron emission tomography (PET) images are useful in clinical practice because they can be used to calculate the metabolic parameters (K) of tissues using graphical methods (such as Patlak plots). K 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 K 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.
In 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 K parameter image.
A quantitative analysis showed that the network-generated K 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.
The 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.
动态正电子发射断层扫描（PET）图像在临床实践中很有用，因为它们可用于通过图形方法（如Patlak图）计算组织的代谢参数（K）。K比标准摄取值更稳定，对临床诊断具有良好的参考价值。然而，获取动态PET图像所需的长时间扫描（通常为一小时）在某些方面使该方法的实用性降低。扫描持续时间与K图像的信噪比（SNR）之间存在权衡。我们研究的目的是在仅半小时内获得与一小时扫描产生的图像大致相同的图像，提高30分钟扫描获得的图像的SNR，并减少获取动态PET图像所需的1小时扫描时间。
在本文中，我们使用U-Net作为特征提取器，以获取有关感兴趣图像结构的先验知识的特征向量，然后利用参数生成器为双组织三室模型获取五个参数并生成时间-活度曲线（TAC），通过训练该曲线将接近原始的1小时TAC。上述生成的动态PET图像最终获得K参数图像。
定量分析表明，与30分钟扫描时间生成的图像相比，网络生成的K参数图分别将结构相似性指数测量值和峰值SNR平均提高了2.27%和7.04%，并将均方根误差（RMSE）降低了16.3%。
所提出的方法是可行的，使用所提出的深度学习方法可以实现令人满意的PET定量准确性。在将这种方法应用于常规临床应用之前，需要进一步的临床验证。
