用于 TOF-PET 列表模式图像重建的深度展开原始对偶网络
Deep unrolled primal dual network for TOF-PET list-mode image reconstruction
AI总结：
本文提出一种用于 TOF-PET 列表模式重建的深度展开原始对偶网络，结合 CUDA 并行加速与动态访问策略降低内存消耗，在低计数等不同条件下视觉与定量分析均优于现有主流重建算法。
摘要：
飞行时间（TOF）信息为湮灭光子提供了更精确的定位数据，从而提高了 PET 重建图像的质量并降低了噪声。列表模式重建在处理 TOF 信息方面具有显著优势。然而，当前先进的 TOF PET 列表模式重建算法在处理低计数数据时仍需改进。深度学习算法在 PET 图像重建中展现出可喜的成果。然而，引入 TOF 信息给深度学习方法所需的存储空间带来了重大挑战，特别是对于先进的深度展开方法。在本研究中，我们提出了一种用于 TOF-PET 列表模式重建的深度展开原始对偶网络。该网络被展开为多个阶段，每个阶段包含一个用于列表模式域更新的对偶网络和一个用于图像域更新的原始网络。我们利用 CUDA 对 TOF 列表模式数据的系统矩阵进行并行加速和计算，并采用动态访问策略以减少内存消耗。不同 TOF 分辨率和不同计数水平下的重建图像表明，所提出的方法在视觉和定量分析上均优于 LM-OSEM、LM-EMTV、LM-SPDHG、LM-SPDHG-TV 和 FastPET 方法。这些结果证明了深度展开方法在 TOF-PET 列表模式数据中的潜在应用，并表现出优于当前主流 TOF-PET 列表模式重建算法的性能，为深度学习方法在 TOF 列表模式数据中的应用提供了新见解。本工作的代码可在 https://github.com/RickHH/LMPDnet 获取
英文摘要：
Time-of-flight (TOF) information provides more accurate location data for annihilation photons, thereby enhancing the quality of 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. In this study, we propose 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, and we adopt a dynamic access strategy to mitigate memory consumption. Reconstructed images of different TOF resolutions and different count levels show that the proposed method outperforms the LM-OSEM, LM-EMTV, LM-SPDHG,LM-SPDHG-TV and FastPET method in both visually and quantitative analysis. These results demonstrate the potential application of deep unrolled methods for TOF-PET list-mode data and show better performance than current mainstream TOF-PET list-mode reconstruction algorithms, providing new insights for the application of deep learning methods in TOF list-mode data. The codes for this work are available at https://github.com/RickHH/LMPDnet
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
