Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET.
Fast and memory-efficient reconstruction of sparse Poisson data in listmode with non-smooth priors with application to time-of-flight PET.
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DOI：
10.1088/1361-6560/ac71f1
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发表时间：
2022-07-27
影响因子：
3.5
通讯作者：
Holler, Martin
中科院分区：
文献类型：
--
作者：
Schramm, Georg;Holler, Martin
关键词：
最先进的TOF PET扫描仪的飞行时间（TOF）的曲目目前具有较大的内存范围。它们的大小将随着可实现的检测器TOF分辨率的进展而继续增加，并使用迭代算法在轴向视野中增加。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，对于更高级的优化算法，例如随机的原始偶发性混合梯度（SPDHG）算法，这是如此。使用保证收敛的子集进行调节的平滑先验。使用常规计算硬件TOF PET系统。
在TOF Sinograph的一般稀疏性质中，我们提出并分析了SPDHG算法的新列表模式（LM）扩展，以根据Poisson分布进行稀疏数据的图像重建。并在最先进的PET/CT系统上获得了一个真正的数据集。和ListMode EM-TV算法。
我们表明，所提出的LM-SPDHG的收敛速度等效于在BINNED数据上运行的原始SPDHG（TOF Sinograms）。对于短的动态框架，LM-SPDHG将所需的内存从约56 GB降低至0.7 GB，具有107个及时巧合的动态框架，而长期静态采集则降低到12.4 GB ·108个及时的巧合。
与SPDHG相比，LM-SPDHG的记忆要求减少了，可以在最新的GPU上实现纯GPU - 避免使用主机和GPU之间的内存转移 - 这将允许重建时间加速。在常规临床实践中，LM-SPDHG的长期重建时间至关重要。
Complete time of flight (TOF) sinograms of state-of-the-art TOF PET scanners have a large memory footprint. Currently, they contain ~4·109 data bins which amount to ~17 GB in 32 bit floating point precision. Moreover, their size will continue to increase with advances in the achievable detector TOF resolution and increases in the axial field of view. 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 stochastic primal-dual hybrid gradient (SPDHG) algorithm which allows for the use of non-smooth priors for regularization 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 using conventional computing hardware. Motivated by the generally sparse nature of the TOF sinograms, we propose and analyze a new listmode (LM) extension of the SPDHG algorithm for image reconstruction of sparse data following a Poisson distribution. The new algorithm is evaluated based on realistic 2D and 3D simulationsn, and a real dataset acquired on a state-of-the-art 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 the proposed LM-SPDHG is equivalent the original SPDHG operating on binned data (TOF sinograms). However, we find that for a TOF PET system with 400 ps TOF resolution and 25 cm axial FOV, the proposed LM-SPDHG reduces the required memory from approximately 56 GB to 0.7 GB for a short dynamic frame with 107 prompt coincidences and to 12.4 GB for a long static acquisition with 5 · 108 prompt coincidences. In contrast to SPDHG, the reduced memory requirements of LM-SPDHG enables a pure GPU implementation on state-of-the-art GPUs - avoiding memory transfers between host and GPU - 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.
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影响因子：
3.5
作者：
Rigie DS;La Rivière PJ
通讯作者：
La Rivière PJ
影响因子：
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作者：
RUDIN, LI;OSHER, S;FATEMI, E
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FATEMI, E
影响因子：
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作者：
Ahn, Sangtae;Leahy, Richard M.
通讯作者：
Leahy, Richard M.
影响因子：
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作者：
Knoll F;Holler M;Koesters T;Otazo R;Bredies K;Sodickson DK
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Sodickson DK
影响因子：
10.6
作者：
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通讯作者：
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