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:由于在单个框架中收到的计数有限,重建动态正电子排放断层摄影图像极具挑战性。在本文件中,我们提议为动态平端图像重建建立一个空间时序原始双网络(STPDnet ) 。空间和时间相关关系由3D变形操作者编码。PET的物理投影嵌入网络的迭代学习过程,提供物理限制,提高可解释性。真正的鼠扫描数据实验显示,拟议方法可以在时空领域实现大幅降低噪音,并超过最大可能性的预期最大化(MLEM )、空间时内核法(KEM-ST )、深PET和已形成原始两极(LPD ) 。</s>