LOR-OSEM: statistical PET reconstruction from raw line-of-response histograms
LOR-OSEM: statistical PET reconstruction from raw line-of-response histograms
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DOI：
10.1088/0031-9155/49/20/005
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发表时间：
2004-10-21
影响因子：
3.5
通讯作者：
Kadrmas, DJ
中科院分区：
文献类型：
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作者：
Kadrmas, DJ
迭代统计重建方法正在成为正电子发射断层成像(PET)的标准方法。传统的最大似然期望最大化(MLEM)和有序子集(OSEM)算法作用于已被预处理成校正的、均匀分布的直方图的数据；然而，这种预处理会破坏泊松统计量。最近的进展将衰减、散射和随机数补偿纳入迭代重建。这项工作的目标是结合剩余的前处理步骤，包括圆弧校正，以直接从原始的不均匀间隔的响应线(LOR)直方图重建。这完全保留了泊松统计信息和完整的空间信息，其方式与列表模式ML密切相关，充分利用了ML统计模型。LOR-Osem算法是使用基于旋转的投影仪实现的，该投影仪直接映射到不均匀间隔的LOR网格。通过模拟和体模实验研究了2DPET的分辨率、对比度和噪声特性。LOR-OEM提供了一种有益的噪声分辨率折衷方案，其表现优于AW-OEM，与AW-OEM的表现相差无几。探讨了LOR-ML算法和LISTMODE ML算法之间的关系，并讨论了它们在实现上的差异。对于基于直方图的重建，LOR-OSEM是一种可行的替代AW-OSEM的方法，具有更好的空间分辨率和噪声特性。
Iterative statistical reconstruction methods are becoming the standard in positron emission tomography (PET). Conventional maximum-likelihood expectation-maximization (MLEM) and ordered-subsets (OSEM) algorithms act on data which have been pre-processed into corrected, evenly-spaced histograms; however, such pre-processing corrupts the Poisson statistics. Recent advances have incorporated attenuation, scatter and randoms compensation into the iterative reconstruction. The objective of this work was to incorporate the remaining pre-processing steps, including arc correction, to reconstruct directly from raw unevenly-spaced line-of-response (LOR) histograms. This exactly preserves Poisson statistics and full spatial information in a manner closely related to listmode ML, making full use of the ML statistical model. The LOR-OSEM algorithm was implemented using a rotation-based projector which maps directly to the unevenly-spaced LOR grid. Simulation and phantom experiments were performed to characterize resolution, contrast and noise properties for 2D PET. LOR-OSEM provided a beneficial noise-resolution tradeoff, outperforming AW-OSEM by about the same margin that AW-OSEM outperformed pre-corrected OSEM. The relationship between LOR-ML and listmode ML algorithms was explored, and implementation differences are discussed. LOR-OSEM is a viable alternative to AW-OSEM for histogram-based reconstruction with improved spatial resolution and noise properties.