Introduction. With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control the data collected and the associated analysis. Attempts to use artificial intelligence (AI) to perform automated quality control (QC) for both single-site and multi-site datasets have been explored in some neuroimaging techniques (e.g. EEG or MRI), although these methods struggle to find replication in other domains. The aim of this study is to test the feasibility of an automated QC pipeline for brain [¹⁸F]-FDOPA PET imaging as a biomarker for the dopamine system.

Methods. Two different Convolutional Neural Networks (CNNs) were used and combined to assess spatial misalignment to a standard template and the signal-to-noise ratio (SNR) relative to 200 static [¹⁸F]-FDOPA PET images that had been manually quality controlled from three different PET/CT scanners. The scans were combined with an additional 400 scans, in which misalignment (200 scans) and low SNR (200 scans) were simulated. A cross-validation was performed, where 80% of the data were used for training and 20% for validation. Two additional datasets of [¹⁸F]-FDOPA PET images (50 and 100 scans respectively with at least 80% of good quality images) were used for out-of-sample validation.

Results. The CNN performance was excellent in the training dataset (accuracy for motion: 0.86 ± 0.01, accuracy for SNR: 0.69 ± 0.01), leading to 100% accurate QC classification when applied to the two out-of-sample datasets. Data dimensionality reduction affected the generalizability of the CNNs, especially when the classifiers were applied to the out-of-sample data from 3D to 1D datasets.

Conclusions. This feasibility study shows that it is possible to perform automatic QC of [¹⁸F]-FDOPA PET imaging with CNNs. The approach has the potential to be extended to other PET tracers in both brain and non-brain applications, but it is dependent on the availability of large datasets necessary for the algorithm training.

介绍。随着生物医学成像研究越来越多地使用大型数据集，找到无需操作员的方法来对收集的数据和相关分析进行质量控制变得至关重要。一些神经影像技术（例如 EEG 或 MRI）已经尝试使用人工智能（AI）对单站点和多站点数据集执行自动质量控制（QC），尽管这些方法很难在其他领域找到复制。本研究的目的是测试大脑 [ ¹⁸ F]-FDOPA PET 成像的自动化 QC 流程作为多巴胺系统生物标志物的可行性。

方法。^{使用并组合两种不同的卷积神经网络 (CNN) 来评估与标准模板的空间错位以及相对于 200 张静态 [ 18} F]-FDOPA PET图像的信噪比 (SNR)，这些图像已通过手动质量控制从三种不同的 PET/CT 扫描仪。这些扫描与另外 400 次扫描相结合，其中模拟了未对准（200 次扫描）和低 SNR（200 次扫描）。进行了交叉验证，其中 80% 的数据用于训练，20% 用于验证。另外两个[ ¹⁸ F]-FDOPA PET 图像数据集（分别具有至少 80% 的优质图像的 50 次和 100 次扫描）用于样本外验证。

结果。CNN 在训练数据集中表现出色（运动准确度：0.86 ± 0.01，SNR 准确度：0.69 ± 0.01），当应用于两个样本外数据集时，QC 分类准确率达到 100%。数据降维影响了 CNN 的泛化能力，尤其是当分类器应用于从 3D 到 1D 数据集的样本外数据时。

结论。这项可行性研究表明，可以使用 CNN对 [ ^{18 F]-FDOPA PET 成像进行自动 QC。}该方法有可能扩展到大脑和非大脑应用中的其他 PET 示踪剂，但它取决于算法训练所需的大型数据集的可用性。