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arXiv 1808.06385physics.med-phphysics.ins-det
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基于梯度树Boosting的PET单晶体DOI定位算法

A novel DOI Positioning Algorithm for Monolithic Scintillator Crystals in PET based on Gradient Tree Boosting

Florian Müller, David Schug, Patrick Hallen, Jan Grahe, Volkmar Schulz

摘要:

在正电子发射断层成像(PET)中,单片晶体被用作分段闪烁体阵列的替代品。整料提供良好的能量、定时和空间分辨率,包括内在相互作用深度(DOI)编码。DOI允许减少PET环内偏心位置处的视差误差(径向散光)。我们提出了一种新的DOI估计方法的基础上监督机器学习算法梯度树提升(GTB)。GTB基于顺序二元比较(决策树)构建预测回归模型。GTB模型已被证明是可实现的FPGA,如果内存需求适合可用的资源。我们提出了两种优化方案,以获得最佳的定位性能:一种是限制可用内存以实现未来的FPGA实现,另一种是没有任何限制。GTB模型的定位性能进行了比较与DOI估计方法的基础上,一个单一的DOI可观测(SO)的文献中提出的其他方法。对于一个12毫米高的整体,我们实现了平均空间分辨率为2.15毫米和2.12毫米半高宽SO和GTB模型,分别。与SO模型相比,GTB模型在整个晶体深度上显示出几乎均匀的定位性能。

英文摘要:

Monolithic crystals are examined as an alternative to segmented scintillator arrays in positron emission tomography (PET). Monoliths provide good energy, timing and spatial resolution including intrinsic depth of interaction (DOI) encoding. DOI allows reducing parallax errors (radial astigmatism) at off-center positions within a PET ring. We present a novel DOI-estimation approach based on the supervised machine learning algorithm gradient tree boosting (GTB). GTB builds predictive regression models based on sequential binary comparisons (decision trees). GTB models have been shown to be implementable in FPGA if the memory requirement fits the available resources. We propose two optimization scenarios for the best possible positioning performance: One restricting the available memory to enable a future FPGA implementation and one without any restrictions. The positioning performance of the GTB models is compared with a DOI estimation method based on a single DOI observable (SO) comparable to other methods presented in literature. For a 12 mm high monolith, we achieve an averaged spatial resolution of 2.15 mm and 2.12 mm FWHM for SO and GTB models, respectively. In contrast to SO models, GTB models show a nearly uniform positioning performance over the whole crystal depth.

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