Datasheet1_Deep learning derived input function in dynamic [18F]FDG PET imaging of mice.pdf

Dynamic positron emission tomography and kinetic modeling play a critical role in tracer development research using small animals. Kinetic modeling from dynamic PET imaging requires accurate knowledge of an input function, ideally determined through arterial blood sampling. Arterial cannulation in mice, however, requires complex, time-consuming and terminal surgery, meaning that longitudinal studies are impossible. The aim of the current work was to develop and evaluate a non-invasive, deep learning based prediction model (DLIF), that directly takes the PET data as input to predict a usable input function. We first trained and evaluated the DLIF model on 68 [18F]Fluorodeoxyglucose mouse scans with image-derived targets using cross validation. Subsequently, we evaluated the performance of a trained DLIF model on an external dataset consisting of 8 mouse scans where the input function was measured by continuous arterial blood sampling. The results showed that the predicted DLIF and image-derived targets were similar, and the net influx rate constants following from Patlak modeling using DLIF as input function were strongly correlated to the corresponding values obtained using the image-derived input function. There were somewhat larger discrepancies when evaluating the model on the external dataset, which could be attributed to systematic differences in the experimental setup between the two datasets. In conclusion, our non-invasive DLIF prediction method may be a viable alternative to arterial blood sampling in small animal [18F]FDG imaging. With further validation, DLIF could overcome the need for arterial cannulation and allow fully quantitative and longitudinal experiments in PET imaging studies of mice.

动态正电子发射断层扫描与动力学模型在利用小型动物进行示踪剂开发研究中扮演着至关重要的角色。从动态正电子发射断层扫描图像中进行的动力学建模需要准确了解输入函数，理想情况下应通过动脉血样采集确定。然而，在老鼠身上进行动脉插管需要复杂、耗时且为终末手术，意味着纵向研究成为不可能。本研究的目的是开发并评估一种非侵入性的基于深度学习的预测模型（DLIF），该模型直接以PET数据作为输入以预测可用的输入函数。我们首先使用交叉验证，在68个[18F]氟代脱氧葡萄糖老鼠扫描图像中，利用图像衍生目标对DLIF模型进行训练和评估。随后，我们评估了在由8个老鼠扫描图像组成的外部数据集上训练的DLIF模型的性能，其中输入函数是通过连续动脉血样采集测量的。结果显示，预测的DLIF和图像衍生目标相似，并且使用DLIF作为输入函数的Patlak建模所得出的净流入率常数与使用图像衍生输入函数获得的相应值高度相关。在外部数据集上评估模型时，存在一定程度的差异，这可能是由于两个数据集在实验设置上的系统差异所致。总之，我们的非侵入性DLIF预测方法可能成为小型动物[18F]FDG成像中动脉血样采集的可行替代方案。经过进一步的验证，DLIF有望克服动脉插管的需求，并允许在老鼠的PET成像研究中进行完全定量的纵向实验。