Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.
Automated model-based quantitative analysis of phantoms with spherical inserts in FDG PET scans.
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DOI：
10.1002/mp.12643
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发表时间：
2018-01
期刊：
影响因子：
3.8
通讯作者：
Beichel RR
中科院分区：
文献类型：
--
作者：
Ulrich EJ;Sunderland JJ;Smith BJ;Mohiuddin I;Parkhurst J;Plichta KA;Buatti JM;Beichel RR
质量控制在定量PET成像中起着越来越重要的作用，通常使用体模进行。本工作的目的是开发和验证两种常见PET/CT质量保证体模（NEMA NU-2 IQ和SNMMI/CTN肿瘤体模）的全自动分析方法。该算法设计为仅利用PET扫描来分析具有薄壁插入物的体模。我们介绍了一种基于模型的方法，用于自动分析的phantomy与球形插入。首先为待分析的每种类型的体模构建模型。一个强大的插入检测算法使用该模型来定位体模内的所有插入。首先，使用尺度空间检测方法检测插入的候选者。其次，使用基于分数的优化算法给候选人一个初始标签。第三，稳健的模型拟合步骤将体模模型与初始标记对齐并修复不正确的标记。最后，对检测到的插入物位置进行细化，并对每个插入物和几个背景区域进行测量。此外，自动选择NEMA和CTN体模模型的方法。在15个NEMA和20个CTN体模PET/CT扫描的不同集合上对该方法进行了评价。NEMA体模以超过背景的9.7：1活性比填充放射性示踪剂溶液，CTN体模以超过背景的4：1和2：1活性比填充。对于定量评价，由两名专家使用体模的PET/CT扫描生成独立的参考标准。此外，由四位专家将自动化方法与代表当前临床标准方法的PET体模扫描手动分析进行了比较。自动分析方法成功检测并测量了所有测试体模扫描中的所有插入物。它是一种确定性算法（零变异性），插入检测RMS误差（即，偏差）分别为0.97、1.12和1.48 mm。对于所有体模和所有对比度，平均RMS误差被发现是显着较低的建议的自动化方法相比，手动分析的体模扫描。通过自动化方法产生的摄取测量结果显示与独立参比标准品具有高度相关性（R2 ≥ 0.9987）。此外，自动化方法的平均计算时间为30.6秒，与手动分析（平均值：247.8秒）相比显著更低（p <0.001）。所提出的自动化方法被发现有较少的误差时，对独立的参考比手动的方法。它可以很容易地适应其他体模与球形插件。此外，它消除了PET体模分析中的操作员间和操作员内差异，并且显著提高了时间效率，因此，代表了一种有前途的方法，可促进和简化PET标准化和协调工作。
Quality control plays an increasingly important role in quantitative PET imaging and is typically performed using phantoms. The purpose of this work was to develop and validate a fully-automated analysis method for two common PET/CT quality assurance phantoms: the NEMA NU-2 IQ and SNMMI/CTN oncology phantom. The algorithm was designed to only utilize the PET scan to enable the analysis of phantoms with thin-walled inserts. We introduce a model-based method for automated analysis of phantoms with spherical inserts. Models are first constructed for each type of phantom to be analyzed. A robust insert detection algorithm uses the model to locate all inserts inside the phantom. First, candidates for inserts are detected using a scale-space detection approach. Second, candidates are given an initial label using a score-based optimization algorithm. Third, a robust model fitting step aligns the phantom model to the initial labeling and fixes incorrect labels. Finally, the detected insert locations are refined and measurements are taken for each insert and several background regions. In addition, an approach for automated selection of NEMA and CTN phantom models is presented. The method was evaluated on a diverse set of 15 NEMA and 20 CTN phantom PET/CT scans. NEMA phantoms were filled with radioactive tracer solution at 9.7:1 activity ratio over background, and CTN phantoms were filled with 4:1 and 2:1 activity ratio over background. For quantitative evaluation, an independent reference standard was generated by two experts using PET/CT scans of the phantoms. In addition, the automated approach was compared against manual analysis, which represents the current clinical standard approach, of the PET phantom scans by four experts. The automated analysis method successfully detected and measured all inserts in all test phantom scans. It is a deterministic algorithm (zero variability), and the insert detection RMS error (i.e., bias) was 0.97, 1.12, and 1.48 mm for phantom activity ratios 9.7:1, 4:1, and 2:1, respectively. For all phantoms and at all contrast ratios, the average RMS error was found to be significantly lower for the proposed automated method compared to the manual analysis of the phantom scans. The uptake measurements produced by the automated method showed high correlation with the independent reference standard (R2 ≥ 0.9987). In addition, the average computing time for the automated method was 30.6 seconds and was found to be significantly lower (p ≪ 0.001) compared to manual analysis (mean: 247.8 seconds). The proposed automated approach was found to have less error when measured against the independent reference than the manual approach. It can be easily adapted to other phantoms with spherical inserts. In addition, it eliminates inter- and intra-operator variability in PET phantom analysis and is significantly more time efficient, and therefore, represents a promising approach to facilitate and simplify PET standardization and harmonization efforts.
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DOI：
10.1007/s00259-013-2391-1
发表时间：
2013-07
影响因子：
9.1
作者：
Lasnon, Charline;Desmonts, Cedric;Quak, Elske;Gervais, Radj;Do, Pascal;Dubos-Arvis, Catherine;Aide, Nicolas
通讯作者：
Aide, Nicolas
影响因子：
19.5
作者：
Lindeberg, T
通讯作者：
Lindeberg, T
影响因子：
2.5
作者：
Fedorov, Andriy;Beichel, Reinhard;Kalpathy-Cramer, Jayashree;Finet, Julien;Fillion-Robin, Jean-Christophe;Pujol, Sonia;Bauer, Christian;Jennings, Dominique;Fennessy, Fiona;Sonka, Milan;Buatti, John;Aylward, Stephen;Miller, James V.;Pieper, Steve;Kikinis, Ron
通讯作者：
Kikinis, Ron
影响因子：
3.5
作者：
Bergmann, H;Dobrozemsky, G;Samal, M
通讯作者：
Samal, M
DOI：
10.1109/34.126805
发表时间：
1992-04-01
影响因子：
23.6
作者：
RATTARANGSI, A;CHIN, RT
通讯作者：
CHIN, RT