Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Math Processing Error]\text{K}_{\text {i}} ( [Math Processing Error]\text{K}_{{1}}{)} . In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.

中文翻译：

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病理先验启发网络进行椎体骨赘识别


脑PET的全面量化需要血液输入功能（IF），传统上这是通过侵入性且耗时的动脉导管手术来实现的，这使得其在临床常规中不可行。本研究提出了一种基于深度学习的方法来估计动态脑 FDG 扫描的输入函数 (DLIF)。使用了长短期记忆与完全连接的网络相结合。用于训练的数据集是通过 uEXPLORER 扫描仪获得的 85 次全身动态扫描生成的。来自 8 个脑区和颈动脉的时间-活动曲线作为模型的输入，标记的 IF 由 CT 图像上定义的升主动脉生成。我们强调动力学建模的拟合优度作为额外的物理损失，以减少偏差和对大型训练样本的需求。 DLIF 与现有方法一起在 RMSE、曲线下面积、区域和参数图像量化方面进行评估。结果表明，与使用现有方法生成的中频相比，所提出的模型可以生成在形状和幅度方面更接近参考中频的中频。使用DLIF计算的所有区域动力学参数均与参考值一致，[数学处理误差]\text{K}_{\text的相关系数为0.961（0.913），相对偏差为1.68±8.74%（0.37±4.93%） {i}} ([数学处理错误]\text{K}_{{1}}{)} 。在视觉外观和量化方面，参数图像也与参考图像高度一致。总之，我们的实验表明，经过训练的模型可以从动态大脑 PET 数据中推断出图像衍生的 IF，从而实现后续可靠的动力学建模。