STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction
This work addresses noise reduction in dynamic PET imaging for medical applications, representing an incremental improvement over prior methods.
The authors tackled dynamic PET image reconstruction by proposing STPDnet, a spatial-temporal convolutional primal dual network that embeds physical projection constraints, achieving substantial noise reduction and outperforming existing methods like MLEM and DeepPET on real rat scan data.
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).