深度展开网络提升TOF-PET低计数图像重建质量
Deep unrolled primal dual network for TOF-PET list-mode image reconstruction
- 将深度展开思想融入TOF-PET列表模式重建，分域迭代优化
- 在低计数场景下，信噪比提升18.6%，图像清晰度显著改善
- 适合高精度医学影像重建研究者，尤其关注低剂量PET应用
飞行时间（TOF）信息可提高湮灭光子定位精度，从而提升PET重建图像质量并降低噪声。列表模式重建在处理TOF信息方面具有显著优势。然而，现有先进TOF PET列表模式重建算法在低计数数据下仍需改进。深度学习方法在PET图像重建中表现出色，但融合TOF信息对存储空间要求高，尤其是先进的深度展开方法。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。该网络被展开为多阶段，每阶段包含一个对偶网络用于列表域更新，一个原始网络用于图像域更新。采用CUDA实现系统矩阵的并行计算，并引入动态访问策略以降低内存消耗。在不同TOF分辨率和计数水平下的重建结果显示，所提方法在视觉与定量分析上均优于LM-OSEM、LM-EMTV、LM-SPDHG、LM-SPDHG-TV及FastPET方法。结果表明，深度展开方法在TOF-PET列表模式数据中具有应用潜力，性能优于当前主流算法，为深度学习在列表模式数据中的应用提供新思路。代码已开源：https://github.com/RickHH/LMPDnet。
原文摘要 · Abstract (English)
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
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