The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques
The development and testing of a digital PET phantom for the evaluation of tumor volume segmentation techniques
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DOI：
10.1118/1.2938518
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发表时间：
2008-07-01
期刊：
影响因子：
3.8
通讯作者：
Pelizzari, Charles A.
中科院分区：
文献类型：
--
作者：
Aristophanous, Michalis;Penney, Bill C.;Pelizzari, Charles A.
近年来，正电子发射断层扫描（PET）图像的准确肿瘤体积分割方法一直在研究中，部分原因是PET在放射治疗计划（RTP）中的使用越来越多。然而，没有一个开发的自动或半自动分割方法被证明是足够可靠的，可以被视为标准。其中一个原因是，没有充分表征和可靠的测试数据来评估这些技术。作者构建了一个数字肿瘤体模来满足这一需求。使用Zubal体模创建体模，作为PET模拟所用SimSET软件的输入。将合成肿瘤放置在Zubal体模的肺中以提供用于分割的目标。作者将重点放在肺部，因为将PET纳入RTP的主要目的是针对非小细胞肺癌。对体模进行了几项测试，以确保其与临床PET扫描的相似性。作者测量了统计量，以比较体模和临床重建中肝脏、肺部和肿瘤中感兴趣区域（ROI）的图像强度分布。使用ROI，他们还测量了自相关函数，以确保图像纹理在临床和体模数据中相似。作者还比较了均匀背景下真实的和模拟的均匀活动球体的强度分布和外观。这些测量值，连同体模与临床扫描的视觉比较，沿着表明模拟体模非常好地模拟了现实。最后，他们调查并量化了分割肿瘤所需的阈值与肿瘤图像强度分布的不均匀性之间的关系。本研究中进行的测试和各种测量证明了体模如何提供一种可靠的方法来测试和研究PET中的肿瘤体积分割。(C)2008年美国医学物理学家协会。
Methods for accurate tumor volume segmentation of positron emission tomography (PET) images have been under investigation in recent years partly as a result of the increased use of PET in radiation treatment planning (RTP). None of the developed automated or semiautomated segmentation methods, however, has been shown reliable enough to be regarded as the standard. One reason for this is that there is no source of well characterized and reliable test data for evaluating such techniques. The authors have constructed a digital tumor phantom to address this need. The phantom was created using the Zubal phantom as input to the SimSET software used for PET simulations. Synthetic tumors were placed in the lung of the Zubal phantom to provide the targets for segmentation. The authors concentrated on the lung, since much of the interest to include PET in RTP is for nonsmall cell lung cancer. Several tests were performed on the phantom to ensure its close resemblance to clinical PET scans. The authors measured statistical quantities to compare image intensity distributions from regions-of-interest (ROIs) placed in the liver, the lungs, and tumors in phantom and clinical reconstructions. Using ROIs they also made measurements of autocorrelation functions to ensure the image texture is similar in clinical and phantom data. The authors also compared the intensity profile and appearance of real and simulated uniform activity spheres within uniform background. These measurements, along with visual comparisons of the phantom with clinical scans, indicate that the simulated phantom mimics reality quite well. Finally, they investigate and quantify the relationship between the threshold required to segment a tumor and the inhomogeneity of the tumor's image intensity distribution. The tests and various measurements performed in this study demonstrate how the phantom can offer a reliable way of testing and investigating tumor volume segmentation in PET. (C) 2008 American Association of Physicists in Medicine.