Evaluation of amplitude-based sorting algorithm to reduce lung tumor blurring in PET images using 4D NCAT phantom.
Evaluation of amplitude-based sorting algorithm to reduce lung tumor blurring in PET images using 4D NCAT phantom.
复制标题
使用 4D NCAT 体模评估基于幅度的排序算法，以减少 PET 图像中的肺部肿瘤模糊。
DOI：
10.1016/j.cmpb.2007.05.004
复制
发表时间：
2007
影响因子：
6.1
通讯作者：
McGoron,Anthony
中科院分区：
文献类型：
--
作者：
Wang,Jiali;Byrne,James;Franquiz,Juan;McGoron,Anthony
目的开发和验证一种基于呼吸幅度的PET分类算法，以纠正呼吸周期的异常。方法和材料利用4D NCAT模型，模拟一个呼吸周期内不同时间的肺部和其他结构的3DPET图像，并添加噪声。为了验证幅度合并算法，使用NCAT体模模拟了一种情况下的五个不同的呼吸周期，以及另一种情况下的五个呼吸周期和五个呼吸幅度。通过计算感兴趣区(ROI)的平均计数，对门控图像和非门控图像以及新的幅度绑定算法与时间绑定算法进行了比较。结果对于16个不同肿瘤大小和不同T/B(肿瘤与背景)的肿瘤，新的分类算法平均改善了8.87±5.10%。随着T/B比和肿瘤大小的减小，由于呼吸引起的图像退化增加。较小的肿瘤直径和较低的T/B比有更大的益处，这表明在发现更多有问题的肿瘤方面有潜在的改进。
Purposedevelop and validate a PET sorting algorithm based on the respiratory amplitude to correct for abnormal respiratory cycles.Method and materialsusing the 4D NCAT phantom model, 3D PET images were simulated in lung and other structures at different times within a respiratory cycle and noise was added. To validate the amplitude binning algorithm, NCAT phantom was used to simulate one case of five different respiratory periods and another case of five respiratory periods alone with five respiratory amplitudes. Comparison was performed for gated and un-gated images and for the new amplitude binning algorithm with the time binning algorithm by calculating the mean number of counts in the ROI (region of interest).Resultsan average of 8.87±5.10% improvement was reported for total 16 tumors with different tumor sizes and different T/B (tumor to background) ratios using the new sorting algorithm. As both the T/B ratio and tumor size decreases, image degradation due to respiration increases. The greater benefit for smaller diameter tumor and lower T/B ratio indicates a potential improvement in detecting more problematic tumors.