State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 310027 Hangzhou, People's Republic of China.
Author to whom any correspondence should be addressed.
Phys Med Biol. 2020 Nov 20;65(23). doi: 10.1088/1361-6560/abc09d.
Dynamic positron emission tomography (PET) imaging can provide information about metabolic changes over time, used for kinetic analysis and auxiliary diagnosis. Existing deep learning-based reconstruction methods have too many trainable parameters and poor generalization, and require mass data to train the neural network. However, obtaining large amounts of medical data is expensive and time-consuming. To reduce the need for data and improve the generalization of network, we combined the filtered back-projection (FBP) algorithm with neural network, and proposed FBP-Net which could directly reconstruct PET images from sinograms instead of post-processing the rough reconstruction images obtained by traditional methods. The FBP-Net contained two parts: the FBP part and the denoiser part. The FBP part adaptively learned the frequency filter to realize the transformation from the detector domain to the image domain, and normalized the coarse reconstruction images obtained. The denoiser part merged the information of all time frames to improve the quality of dynamic PET reconstruction images, especially the early time frames. The proposed FBP-Net was performed on simulation and real dataset, and the results were compared with the state-of-art U-net and DeepPET. The results showed that FBP-Net did not tend to overfit the training set and had a stronger generalization.
动态正电子发射断层扫描（PET）成像可以提供随时间变化的代谢变化信息，用于动力学分析和辅助诊断。现有的基于深度学习的重建方法具有过多的可训练参数和较差的泛化能力，需要大量数据来训练神经网络。然而，获取大量医疗数据既昂贵又耗时。为了减少对数据的需求并提高网络的泛化能力，我们将滤波反投影（FBP）算法与神经网络相结合，提出了 FBP-Net，它可以直接从投影数据重建 PET 图像，而不是对传统方法获得的粗略重建图像进行后处理。FBP-Net 包含两部分：FBP 部分和去噪器部分。FBP 部分自适应地学习频率滤波器，实现从探测器域到图像域的转换，并对获得的粗略重建图像进行归一化。去噪器部分融合了所有时间帧的信息，以提高动态 PET 重建图像的质量，尤其是早期时间帧。在模拟和真实数据集上对所提出的 FBP-Net 进行了评估，并将结果与最先进的 U-net 和 DeepPET 进行了比较。结果表明，FBP-Net 不易过度拟合训练集，具有更强的泛化能力。
