STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction

• URL: http://arxiv.org/abs/2303.04667v1
• Date: Wed, 8 Mar 2023 15:43:15 GMT
• Title: STPDnet: Spatial-temporal convolutional primal dual network for dynamic PET image reconstruction
• Authors: Rui Hu, Jianan Cui, Chengjin Yu, Yunmei Chen, Huafeng Liu
• Abstract summary: We propose a spatial-temporal convolutional primal dual network (STPDnet) for dynamic PET image reconstruction. The physical projection of PET is embedded in the iterative learning process of the network. Experiments have shown that the proposed method can achieve substantial noise in both temporal and spatial domains.
• Score: 16.47493157003075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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).

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