论文标题
列表模式中稀疏泊松数据的快速和记忆有效的重建，具有非平滑先验的先验，并应用于飞行时间
Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET
论文作者
论文摘要
最先进的TOF PET扫描仪的完整飞行时间（TOF）正式图具有较大的记忆足迹。当前，它们包含〜4E9数据箱，在32位浮点精度中占约17GB。由于记忆要求和评估每个数据箱的正向模型所需的计算时间，使用迭代算法重建此类巨大的纹章图变得越来越具有挑战性。对于更高级的优化算法（例如SPDHG算法）尤其如此，该算法允许使用具有保证收敛性的子集使用非平滑先验。 SPDHG需要在内存中存储其他正式图，这将其应用严重限制在最新的TOF PET系统中的数据集中。 由TOF Sinograms的普遍稀疏性质激励，我们提出并分析了SPDHG算法的新列表模式（LM）扩展，用于重建泊松分布后稀疏数据。根据2D和3D模拟对新算法进行评估，并在最近的TOF PET/CT系统上获取了实际数据集。将新提出的LM SPDHG算法的性能与常规的Sinogram SPDHG和ListMode EM-TV算法进行了比较。 我们表明，使用BINNED数据，LM-SPDHG的收敛速度等效于原始SPDHG。但是，我们发现，对于具有400PS TOF分辨率的TOF PET系统，LM-SPDHG将所需的存储器从〜56GB降低至0.7GB，对于具有1E7计数的短动态帧，对于5E8计数，长期静态习得。 与SPDHG相反，LM-SPDHG的记忆需求减少，可以在最先进的GPU上实现纯GPU，这将大大加速重建时间。反过来，这将允许LM-SPDHG在常规的临床实践中应用，在短期重建时间至关重要的情况下。
Complete time of flight (TOF) sinograms of state-of-the-art TOF PET scanners have a large memory footprint. Currently, they contain ~4e9 data bins which amount to ~17GB in 32bit floating point precision. Using iterative algorithms to reconstruct such enormous TOF sinograms becomes increasingly challenging due to the memory requirements and the computation time needed to evaluate the forward model for every data bin. This is especially true for more advanced optimization algorithms such as the SPDHG algorithm which allows for the use of non-smooth priors using subsets with guaranteed convergence. SPDHG requires the storage of additional sinograms in memory, which severely limits its application to data sets from state-of-the-art TOF PET systems. Motivated by the generally sparse nature of the TOF sinograms, we propose and analyze a new listmode (LM) extension of the SPDHG algorithm for reconstruction of sparse data following a Poisson distribution. The new algorithm is evaluated based on 2D and 3D simulations, and a real dataset acquired on a recent TOF PET/CT system. The performance of the newly proposed LM SPDHG algorithm is compared against the conventional sinogram SPDHG and the listmode EM-TV algorithm. We show that the speed of convergence of LM-SPDHG is equivalent the original SPDHG using binned data. However, we find that for a TOF PET system with 400ps TOF resolution, LM-SPDHG reduces the required memory from ~56GB to 0.7GB for a short dynamic frame with 1e7 counts and to 12.4GB for a long static acquisition with 5e8 counts. In contrast to SPDHG, the reduced memory requirements of LM-SPDHG enable a pure GPU implementation on state-of-the-art GPUs which will substantially accelerate reconstruction times. This in turn will allow the application of LM-SPDHG in routine clinical practice where short reconstruction times are crucial.
