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A cautionary note on the use of positivity constrained reconstructions for quantification of regional PET imaging data

机译:关于使用正负约束重建量化区域PET成像数据的警告提示

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Positively constrained maximum likelihood (ML) reconstructions in PET eliminate the negative values associated with unconstrained least squares (LS) - more commonly known as filtered back-projection (FBP). This is desirable for certain qualitative imaging tasks, however, it is not clear if there is a significant benefit for quantitative analysis of dynamic data. We consider a situation where the goal is to quantify the mean uptake in a tissue region of interest using data reconstructed with or without positivity constraints. A theoretical analysis is used to show that averaging unconstrained data is a sufficient statistic for estimation of the regional mean. This calculation casts some doubt over averaging constrained data. We use simulation sto investigate the effect of positivity constraint on mixture model analysis of dynamic data. The results show that the positivity constraint may cause bias in estimation of physiological parameters.
机译:PET中的正约束最大似然(ML)重建消除了与无约束最小二乘(LS)相关的负值-更通常称为滤波反投影(FBP)。对于某些定性成像任务而言,这是理想的,但是尚不清楚动态数据的定量分析是否具有显着优势。我们考虑一种情况,目标是使用在有或没有阳性约束条件下重建的数据来量化感兴趣组织区域中的平均摄入量。使用理论分析表明,对不受约束的数据求平均值对于估计区域均值是足够的统计量。这种计算对平均约束数据产生了一些怀疑。我们使用仿真来研究正约束对动态数据混合模型分析的影响。结果表明,阳性约束可能会导致生理参数估计的偏差。

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