论文标题
宠物成像中非阴性执行的本地均值保存后处理步骤：应用于$^{90} $ y-pet
Local-mean preserving post-processing step for non-negativity enforcement in PET imaging: application to $^{90}$Y-PET
论文作者
论文摘要
在低统计的宠物成像环境中，低活动区域的积极偏见是一个燃烧的问题。为了克服这个问题，可以使用没有内置非阴性约束的算法。它们允许图像中的负体素减少甚至取消偏差。但是，这种算法会增加差异，并且难以解释，因为负放射性浓度没有物理含义。在这里，我们提出了一种后处理策略，以消除负面强度，同时保留本地平均活动。我们的想法是将负强度转移到相邻的体素中，以便保留图像的平均值。提出的后处理算法通过特定的对称结构解决了线性编程问题，并且可以以非常有效的方式计算解决方案。从YTTrium-90幻影获得的数据表明，在由非约束算法产生的图像上，在后处理步骤后，在冷区域中获得了较低的差异。
In a low-statistics PET imaging context, the positive bias in regions of low activity is a burning issue. To overcome this problem, algorithms without the built-in non-negativity constraint may be used. They allow negative voxels in the image to reduce, or even to cancel the bias. However, such algorithms increase the variance and are difficult to interpret, since negative radioactive concentrations have no physical meaning. Here, we propose a post-processing strategy to remove negative intensities while preserving the local mean activities. Our idea is to transfer the negative intensities to neighboring voxels, so that the mean of the image is preserved. The proposed post-processing algorithm solves a linear programming problem with a specific symmetric structure, and the solution can be computed in a very efficient way. Acquired data from an yttrium-90 phantom show that on images produced by a non-constrained algorithm, a much lower variance in the cold area is obtained after the post-processing step.
