Dynamic positron emission tomography (dPET) image reconstruction is extremely challenging due to the limited counts received in individual frame. In this paper, we propose a spatial-temporal convolutional primal dual network (STPDnet) for dynamic PET image reconstruction. Both spatial and temporal correlations are encoded by 3D convolution operators. The physical projection of PET is embedded in the iterative learning process of the network, which provides the physical constraints and enhances interpretability. The experiments of real rat scan data have shown that the proposed method can achieve substantial noise reduction in both temporal and spatial domains and outperform the maximum likelihood expectation maximization (MLEM), spatial-temporal kernel method (KEM-ST), DeepPET and Learned Primal Dual (LPD).

翻译：动态正电子发射断层扫描（dPET）图像重建因各帧接收的计数有限而极具挑战性。本文提出一种用于动态PET图像重建的时空卷积原始对偶网络（STPDnet）。通过3D卷积算子对空间和时间相关性进行编码，PET的物理投影被嵌入网络的迭代学习过程中，从而提供物理约束并增强可解释性。真实大鼠扫描数据的实验表明，所提方法能在时空域实现显著降噪，并优于最大似然期望最大化（MLEM）、时空核方法（KEM-ST）、DeepPET及学习型原始对偶网络（LPD）。