量化列表模式数据分箱过程中的信息损失。
Quantifying the loss of information from binning list-mode data.
作者信息
Clarkson Eric, Kupinski Meredith
出版信息
J Opt Soc Am A Opt Image Sci Vis. 2020 Mar 1;37(3):450-457. doi: 10.1364/JOSAA.375317.
Abstract
List-mode data are increasingly being used in single photon emission computed tomography (SPECT) and positron emission tomography (PET) imaging, among other imaging modalities. However, there are still many imaging designs that effectively bin list-mode data before image reconstruction or other estimation tasks are performed. Intuitively, the binning operation should result in a loss of information. In this work, we show that this is true for Fisher information and provide a computational method for quantifying the information loss. In the end, we find that the information loss depends on three factors. The first factor is related to the smoothness of the mean data function for the list-mode data. The second factor is the actual object being imaged. Finally, the third factor is the binning scheme in relation to the other two factors.
摘要
列表模式数据在单光子发射计算机断层扫描（SPECT）和正电子发射断层扫描（PET）成像以及其他成像模态中越来越多地被使用。然而，仍有许多成像设计在进行图像重建或其他估计任务之前有效地对列表模式数据进行分箱。直观地说，分箱操作应该会导致信息丢失。在这项工作中，我们表明对于费希尔信息确实如此，并提供了一种量化信息丢失的计算方法。最后，我们发现信息丢失取决于三个因素。第一个因素与列表模式数据的平均数据函数的平滑度有关。第二个因素是正在成像的实际物体。最后，第三个因素是与其他两个因素相关的分箱方案。
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