Deep unrolled primal dual network for TOF-PET list-mode image reconstruction

作者：Rui Hu, Chenxu Li, Kun Tian, Jianan Cui, Yunmei Chen, Huafeng Liu · 发表于：Physics in Medicine and Biology · 年份：2025 · DOI：10.1088/1361-6560/adf9b7 · 被引用次数：2 · 研究领域：Medical Imaging Techniques and Applications、Advanced MRI Techniques and Applications、Atomic and Subatomic Physics Research

Abstract Objective. Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of positron emission tomography (PET) reconstruction images and reducing noise. List-mode reconstruction has a significant advantage in handling TOF information. However, current advanced TOF-PET list-mode reconstruction algorithms still require improvements when dealing with low-count data. Deep learning algorithms have shown promising results in PET image reconstruction. Nevertheless, the incorporation of TOF information poses significant challenges related to the storage space required by deep learning methods, particularly for the advanced deep unrolled methods. Approach. In this study, we propose LMPDnet, a deep unrolled primal dual network for TOF-PET list-mode reconstruction. The network is unrolled into multiple phases, with each phase comprising a dual network for list-mode domain updates and a primal network for image domain updates. We utilize CUDA for parallel acceleration and computation of the system matrix for TOF list-mode data. Main results. Reconstructed images of different TOF resolutions and different count levels show that the proposed method shows better noise suppression and image quality compared to the list-mode ordered subset expectation maximization, total-variation regularized list-mode expectation maximization, list-mode stochastic primal dual hybrid gradient, total-variation regularized stochastic prima...