Positron emission tomography (PET) serves as an essential tool for diagnosis of encephalopathy and brain science research. However, it suffers from the limited choice of tracers. Nowadays, with the wide application of PET imaging in neuropsychiatric treatment, 6-18F-fluoro-3, 4-dihydroxy-L-phenylalanine (DOPA) has been found to be more effective than 18F-labeled fluorine-2-deoxyglucose (FDG) in the field. Nevertheless, due to the complexity of its preparation and other limitations, DOPA is far less widely used than FDG. To address this issue, a tracer conversion invertible neural network (TC-INN) for image projection is developed to map FDG images to DOPA images through deep learning. More diagnostic information is obtained by generating PET images from FDG to DOPA. Specifically, the proposed TC-INN consists of two separate phases, one for training traceable data, the other for rebuilding new data. The reference DOPA PET image is used as a learning target for the corresponding network during the training process of tracer conversion. Meanwhile, the invertible network iteratively estimates the resultant DOPA PET data and compares it to the reference DOPA PET data. Notably, the reversible model employs variable enhancement technique to achieve better power generation. Moreover, image registration needs to be performed before training due to the angular deviation of the acquired FDG and DOPA data information. Experimental results exhibited excellent generation capability in mapping between FDG and DOPA, suggesting that PET tracer conversion has great potential in the case of limited tracer applications.

翻译：正电子发射断层扫描（PET）是脑病诊断和脑科学研究的重要工具，但其受限于示踪剂的有限选择。当前，随着PET成像在神经精神治疗中的广泛应用，6-18F-氟-3,4-二羟基-L-苯丙氨酸（DOPA）在该领域被证实比18F标记的氟-2-脱氧葡萄糖（FDG）更具临床价值。然而，由于制备工艺复杂及其他局限性，DOPA的应用远不及FDG广泛。为解决这一问题，本文开发了一种用于图像投影的示踪剂转换可逆神经网络（TC-INN），通过深度学习实现FDG图像到DOPA图像的映射。通过从FDG到DOPA的PET图像生成，可获得更多诊断信息。具体而言，所提出的TC-INN包含两个独立阶段：第一阶段用于训练可追溯数据，第二阶段用于重建新数据。在示踪剂转换训练过程中，参考DOPA PET图像被用作对应网络的学习目标，同时可逆网络通过迭代估计生成的DOPA PET数据，并与参考DOPA PET数据进行比较。值得注意的是，该可逆模型采用变量增强技术以提升生成性能。此外，由于采集的FDG和DOPA数据存在角度偏差，训练前需进行图像配准。实验结果表明，该方法在FDG与DOPA之间的映射中展现出优异的生成能力，这意味着在示踪剂应用受限的情况下，PET示踪剂转换具有巨大潜力。