Objectives

Many molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available.

Material and methods

After presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance.

Results

The results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test.

Conclusion

This paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.

中文翻译：

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比较图像中的两个自举区域：D 测试

目标

许多分子成像诊断涉及比较图像或不同图像中的两个感兴趣区域 (ROI)。由于图像是通过测量随机现象获得的，因此这种比较应基于统计测试以确保可靠性。最近的研究表明，使用引导方法可以获取分子图像中重建值的统计变异性。然而，尽管人们普遍认为信息的增加应该使基于分子图像的诊断更加可靠，但相关文献中尚未提出使用引导重复来增强两个 ROI 比较的可靠性的方法。在本文中，我们建议通过引入第一个统计测试来填补这一空白，该测试允许我们比较可进行引导复制的两组像素/体素。

材料与方法

在介绍了这种非参数统计检验的理论基础之后，本文介绍了如何在实践中进行计算。最后，提出了两个基于定量比较和专家判断的实验来评估其相关性。

结果

所得结果与专家对综合数据的诊断一致。这验证了 D 测试的相关性。

结论

本文提出了第一个统计测试来比较重建图像中的两个 ROI，其中可以访问统计变异性信息。