论文标题
通过假节点方法降低多模式医学成像中的吉布斯效应
Reducing the Gibbs effect in multimodal medical imaging by the Fake Nodes Approach
论文作者
论文摘要
在多模式医学成像中，这是一种常见的做法，可以调低解剖学衍生的分割图像，以测量共获得功能图像的平均活性。这种做法避免了与重采样功能图像时会发生的重采样相关的吉布斯效应。随着侧面的影响，由于在许多小时的计算或手动工作中进行了全面分辨率的解剖分割，因此会产生浪费时间和努力。在这项工作中，我们解释了常用的重采样方法，并在连续和不连续的信号的情况下给出错误。然后，我们提出了一个假节点方案，用于重新采样，旨在减少吉布斯的效果，以降低功能图像。在两个重要的实验中，这种新方法与传统的方法进行了比较，这两者都表明假节点重新采样会产生较小的错误。
It is a common practice in multimodal medical imaging to undersample the anatomically-derived segmentation images to measure the mean activity of a co-acquired functional image. This practice avoids the resampling-related Gibbs effect that would occur in oversampling the functional image. As sides effect, waste of time and efforts are produced since the anatomical segmentation at full resolution is performed in many hours of computations or manual work. In this work we explain the commonly-used resampling methods and give errors bound in the cases of continuous and discontinuous signals. Then we propose a Fake Nodes scheme for image resampling designed to reduce the Gibbs effect when oversampling the functional image. This new approach is compared to the traditional counterpart in two significant experiments, both showing that Fake Nodes resampling gives smaller errors.
