标题： 一种用于动态小动物$\left[^{18}\text{F}\right]$FDG PET 成像中动脉输入函数预测的鲁棒且通用的深度学习模型 显示英文标题

标题： A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging

摘要： 动态正电子发射断层扫描（PET）和动力学建模在小动物研究中的示踪剂开发研究中起着关键作用。 准确的动力学建模需要精确的输入函数估计，传统上通过动脉血液采样实现。 然而，在小动物如小鼠中进行动脉插管涉及复杂、耗时且终末性的程序，阻碍了纵向研究。 本研究提出了一种非侵入性、完全卷积的深度学习方法（FC-DLIF），直接从PET成像预测输入函数，可能消除动态小动物PET中血液采样的需求。 所提出的FC-DLIF模型包括一个空间特征提取器，作用于PET序列的体积时间帧，提取空间特征。 这些特征随后在时间特征提取器中进一步处理，以预测动脉输入函数。 该方法使用[$^{18}$F]FDG数据的图像和动脉血液曲线进行交叉验证进行训练和评估。 此外，还使用两种额外放射性示踪剂（[$^{18}$F]FDOPA和[$^{68}$Ga]PSMA）收集的成像数据和动脉血液曲线评估了模型的适用性。 该模型还对时间截断和偏移的数据进行了评估，以模拟较短和偏移的PET扫描。 所提出的FC-DLIF模型在均方误差和相关性方面可靠地预测动脉输入函数。 此外，FC-DLIF模型即使从截断和偏移的样本中也能预测动脉输入函数。 当使用不同放射性示踪剂收集的样本时，该模型无法预测AIF，因为这些样本未在训练数据中出现。 我们的基于深度学习的输入函数为动脉血液采样提供了一种非侵入性和可靠的选择，证明其对时间偏移和不同扫描持续时间具有鲁棒性和灵活性。 显示英文摘要

摘要： Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer development research in small animal studies. Accurate kinetic modeling requires precise input function estimation, traditionally achieved via arterial blood sampling. However, arterial cannulation in small animals like mice, involves intricate, time-consuming, and terminal procedures, precluding longitudinal studies. This work proposes a non-invasive, fully convolutional deep learning-based approach (FC-DLIF) to predict input functions directly from PET imaging, potentially eliminating the need for blood sampling in dynamic small-animal PET. The proposed FC-DLIF model includes a spatial feature extractor acting on the volumetric time frames of the PET sequence, extracting spatial features. These are subsequently further processed in a temporal feature extractor that predicts the arterial input function. The proposed approach is trained and evaluated using images and arterial blood curves from [$^{18}$F]FDG data using cross validation. Further, the model applicability is evaluated on imaging data and arterial blood curves collected using two additional radiotracers ([$^{18}$F]FDOPA, and [$^{68}$Ga]PSMA). The model was further evaluated on data truncated and shifted in time, to simulate shorter, and shifted, PET scans. The proposed FC-DLIF model reliably predicts the arterial input function with respect to mean squared error and correlation. Furthermore, the FC-DLIF model is able to predict the arterial input function even from truncated and shifted samples. The model fails to predict the AIF from samples collected using different radiotracers, as these are not represented in the training data. Our deep learning-based input function offers a non-invasive and reliable alternative to arterial blood sampling, proving robust and flexible to temporal shifts and different scan durations.