PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods.
PETSTEP: Generation of synthetic PET lesions for fast evaluation of segmentation methods.
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DOI：
10.1016/j.ejmp.2015.07.139
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发表时间：
2015-12
期刊：
影响因子：
--
通讯作者：
Schmidtlein CR
中科院分区：
文献类型：
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作者：
Berthon B;Häggström I;Apte A;Beattie BJ;Kirov AS;Humm JL;Marshall C;Spezi E;Larsson A;Schmidtlein CR
这项工作介绍了PETSTEP（PET模拟器的示踪剂通过发射投影）：一个更快，更容易替代蒙特卡罗（MC）模拟生成逼真的PET图像，研究评估图像特征和分割技术。PETSTEP在Matlab中作为开源软件实现。它允许从PET/CT数据或合成CT和PET图生成三维PET图像，并具有用户绘制的病变和用户设置的采集和重建参数。PETSTEP用于再现在GE Discovery 690 PET/CT扫描仪上采集的NEMA体模图像，并使用GE Discovery LS扫描仪的MC进行模拟，并生成逼真的头颈部扫描。最后比较了三种自动分割方法应用于扫描仪采集和PETSTEP模拟NEMA图像时的灵敏度（S）和阳性预测值（PPV）。PETSTEP在单核2.7GHz计算机上分别在4 min和6 min内生成三维体模和临床图像。NEMA体模的PETSTEP图像的平均强度在扫描仪采集的背景和最大插入图像的2%范围内，背景半峰全宽大于16%。当比较PETSTEP图像与MC模拟数据时，获得了类似的结果。使用模拟体模图像获得的S和PPV在统计学上显著低于原始图像，但在评价的分割方法方面得出相同的结论。PETSTEP允许快速模拟合成图像再现扫描仪采集的PET数据，并显示出很大的前景PET分割方法的评估。
This work describes PETSTEP (PET Simulator of Tracers via Emission Projection): a faster and more accessible alternative to Monte Carlo (MC) simulation generating realistic PET images, for studies assessing image features and segmentation techniques. PETSTEP was implemented within Matlab as open source software. It allows generating three-dimensional PET images from PET/CT data or synthetic CT and PET maps, with user-drawn lesions and user-set acquisition and reconstruction parameters. PETSTEP was used to reproduce images of the NEMA body phantom acquired on a GE Discovery 690 PET/CT scanner, and simulated with MC for the GE Discovery LS scanner, and to generate realistic Head and Neck scans. Finally the sensitivity (S) and Positive Predictive Value (PPV) of three automatic segmentation methods were compared when applied to the scanner-acquired and PETSTEP-simulated NEMA images. PETSTEP produced 3D phantom and clinical images within 4 and 6 min respectively on a single core 2.7 GHz computer. PETSTEP images of the NEMA phantom had mean intensities within 2% of the scanner-acquired image for both background and largest insert, and 16% larger background Full Width at Half Maximum. Similar results were obtained when comparing PETSTEP images to MC simulated data. The S and PPV obtained with simulated phantom images were statistically significantly lower than for the original images, but led to the same conclusions with respect to the evaluated segmentation methods. PETSTEP allows fast simulation of synthetic images reproducing scanner-acquired PET data and shows great promise for the evaluation of PET segmentation methods.